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Article

Examining the Sustainable Impact of the Relationship Among the Variables Influencing Sugar-Sweetened Beverage Intake on Sugar Tax

by
Rawlings Obenembot Enowkenwa
1,*,
Saratiel Wedzarai Musvoto
2 and
Fortune Ganda
1
1
Department of Management Accounting and Finance, Faculty of Economic and Finance Sciences, Mthatha Campus, Walter Sisulu University, Mthatha 5117, South Africa
2
Graduate School of Business Leadership, University of South Africa, Pretoria 0003, South Africa
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(16), 7474; https://doi.org/10.3390/su17167474
Submission received: 5 May 2025 / Revised: 2 July 2025 / Accepted: 21 July 2025 / Published: 19 August 2025
(This article belongs to the Special Issue Impacts of Human Activities on Ecosystem Health and Landscape Sustainability)
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Abstract

Sugar-sweetened beverages (SSBs) are among the most traded and a significant component of global food and beverages. The consumption of these beverages is widely believed to be a major contributing factor to overweight, diabetes, tooth decay, and other noncommunicable diseases. To reduce the intake of these beverages, the World Health Organisation (WHO) encouraged countries and jurisdictions to introduce a sugar tax policy as an approach to reduce the sales and intake of the beverages. The purpose of this study is to evaluate the sustainability of the relationship that exists among the factors that influence the intake of SSBs in enhancing sugar tax in South Africa. A mixed research methods were used to explore the relationships among the variables. The Exploratory Sequential Design (ESD) was deemed appropriate to deal with the introduction of a sugar tax to reduce the intake of the SSB, most especially in Africa where the tax is a new concept. The Exploratory Sequential Design began with the collection of the structured interview qualitative data and analysis using the thematic analysis procedure, then followed by quantitative data collection and analysis using the confirmatory factor analysis method. This study used mainly primary data collected from the Gauteng Province of South Africa for both the qualitative and quantitative phases of the study. The study found that a sustainable effective sugar tax can be achieved when the public is aware of the existence, purpose, and acceptance of the sugar tax. Furthermore, the tax can become relevant and sustainable when it leads to a significant reduction in intake, contributing to negative consumer behaviour and attitude towards the intake of SSBs in South Africa. A synthesis of the integrated results confirmed that the recognition of the relationship among the factors influencing the intake of SSB and penalising the beverage manufacturers who do not reduce the sugar content in all their beverages as recommended by the WHO are vital in leading to a sustainable enhancement of an effective sugar tax in South Africa.

Graphical Abstract

1. Introduction

Sugar-sweetened beverages (SSBs) are among the most traded commodities globally, and a significant component of global food [1]. According to the WHO (2015), its exports account for over one-quarter of global production, but much controversy exists in the high protection given to sugary products [2]. The consumption of SSBs is widely believed to be a major contributing factor to overweight, type 2 diabetes, tooth decay, and obesity [3,4,5,6]. There is evidence linking excessive intake of SSBs to weight gain, obesity, and productivity rate [7]. Obesity and diabetes are global pandemics and have become the leading causes of mortality worldwide [8]. A sugar-sweetened beverage is any beverage that contains added sugar and or other caloric sweeteners, such as high-fructose corn syrup and fruit juice concentrates [9]. According to Bawadi et al. (2019) and Véliz et al. (2019), the United Nations in 2011 made a recommendation to governments worldwide to use a sugar tax policy to regulate the diet and health of individuals [10,11]. Research has indicated that reducing high-calorie beverage intake may reduce obesity and other noncommunicable diseases [12,13]. Sugar tax can reduce the intake of SSB, beverage reformulation by manufacturers, as well as generate revenue for governments [14]. In Mexico, evidence by Colchero et al. (2017) and Itria et al. (2021) found that two years after the introduction of the tax, the low- and middle-income households’ intake of SSB decreased by 11.7% and 7.6%, respectively [15,16]. Nevertheless, other studies indicate that even though the sugar tax reduces the intake of SSBs for some adults, the intake by other groups of individuals has not declined significantly [17].
Despite the proposal and implementation of the tax in some counties and jurisdictions [18], the tax remains controversial in some economies [19], especially when it was repealed in Cook County (USA) and Denmark [20,21]. In the event of introducing a sugar tax policy in South Africa, policymaker(s) neglected the critical role of the factors that influence the intake of SSBs and the role of other stakeholders in the build-up to an effective sugar tax. To date, revenue generated from the sales and consumption of SSBs is on the increase [22]. It should be noted, however, that a tax on sweetened beverages and mineral water was previously introduced in South Africa in 1994 and scrapped from legislation in 2002. The tax was abandoned in 2002 after lobbying efforts by the concerned industry. Recent studies indicate that various stakeholders are calling for the sugar tax rate to be increased to about 20–25% in order to realise a significant decrease in the intake of SSBs [23,24]. This study seeks to examine the sustainability of the relationships that exist among the variables influencing the intake of SSBs and their impact on the sugar tax in South Africa. In a quest to draft an inclusive and sustainable sugar tax policy, more studies are required to add to the limited body of knowledge regarding the consumption and the relationship among the factors that influence the intake of SSBs in South Africa. The use of fiscal policy alone has proven to be ineffective in reducing intake and obesity; therefore, there is a need for a wider intervention initiative.

2. Literature Review

The intention of the WHO for a sugar tax is to make these beverages more expensive and to reduce their intake [2,11]. However, some studies on the sugar tax revealed unrealistic assumptions in some economies. Studies by Arundhana et al. (2018), Miller et al. (2020), and Eykelenboom et al. (2019) associated the increased consumption of SSB with factors such as insignificant price increase, as well as socioeconomic, dietary, psycho-social, and advertisement influences [25,26,27]. Nevertheless, Hattersley et al. (2020) indicated that the sugar tax led to a decline in consumption from 4.4 g/100 mL to 2.9 g/100 mL, as well as an increase in purchases by 10% for pure fruit juice, diet drinks, and bottled water [28]. In addition, Scarborough et al. (2020) found evidence of significant reformulation by manufacturers to lower the sugar content in some beverages [29]. According to Stacey et al. (2019), revenue generated from the sugar tax in South Africa exceeded the forecasts, despite evidence that it has resulted in a decrease in intake [30].
The obstacle to implementing a sustainable sugar tax policy is suggested to be policymakers’ hesitancy and inherent fear of civil protests in reaction to the increasing prices of SSBs [31,32]. Another challenge has been the resistance from the SSB industry, as they have purposely diverted attention to the tax being discriminatory in nature and questioned its sustainability over time [33]. According to Andreyeva et al. (2022), a significant increase in the price of taxed beverages and a pass-through rate of about 82% have been implemented across majority of the countries and jurisdictions that have introduced the tax [34]. Encouragingly, to countries and jurisdictions that have increased prices significantly, a sustainable reduction in purchases and consumption has been witnessed [35,36].
According to Subaiea et al. (2019), Yamoah et al. (2021), Marmorstein (2019), and Fernandes et al. (2020), consumers have attributed the popularity and intake of SSB to massive advertising (46.7%), fatigue relief (64.6%), and increased alertness and focus (75.8%) [37,38,39,40]. The study by Stacey et al. (2017) found that the intake of energy SSBs is higher among males, at 0.591, than for females, at 0.445 per week [41]. On the contrary, Essman et al. (2022) did not find any disparity in energy beverage intake among age and gender and from different socioeconomic groups [42]. To create awareness and sustainable reduction in intake of SSBs, Tahmassebi and BaniHani (2019) suggested the banning of SSB advertisements and significant price increases [43]. According to Lloyd and MacLaren (2019) as well as Vieux et al. (2020), a corrective tax should be designed with the purpose of increasing national awareness and welfare [44,45].
There is evidence that the disposable income of individuals or households has an impact on the intake of SSBs [46]. The general notion is that the higher the disposable income, the fewer inferior goods consumed [47]. Households with income below $25,000 p/a in the USA consume 200 calories p/d, while those with income above $75,000 p/a consume approximately 117 calories p/d [46]. On the other hand, the decline in disposable income, coupled with the increased household size in Egypt, resulted in the increased intake of low-quality food and beverages [48].
In assessing the impact of price increase on SSB intake, Fernandez and Raine (2019) found that a $1 increase in price of a 2-litre bottle of SSB resulted in a reduction in obesity by 28.1% and 10.8% in women and men, respectively [49]. Similarly, a Canadian modelling study predicted that a 20% sugar tax would prevent approximately 700,000 cases of overweight and generate $1.7 billion in sugar tax revenue [50]. Meanwhile, in France, the price increase by 5% in 2012 and 3.1% in 2013 resulted in a decrease in intake by 3.3% and 3.4%, respectively [51]. In Zimbabwe, Hangoma et al. (2020) predicted that a 25% sugar tax rate can reduce intake and lead to health benefits, particularly among the adult female population [52]. Regarding the impact of age on consumption, Newens and Walton (2016) found that intake ranged from 6.3 to 11.2% for 60–70-year-old men in Norway, New Zealand, and the United Kingdom (UK) [53]. In contrast, Amoutzopoulos et al. (2020) indicated that a greater proportion of the adults in the UK (19 to 64 years) adhered to the recommendations by the WHO (2015) when compared to teenagers (11 to 18 years) [54]. On the other hand, a cross-sectional survey of adults intake (age 18–39) in South Africa found that the sugar tax resulted in a decline in intake of SSBs just one year after the implementation of the tax [42]. The majority of the studies in African countries indicated that about 46.7% of the children aged 2–18 years consume SSB daily, with youths consuming on average 217 mL p/d [17,55].
A systematic review study of 17 selected research articles from PubMed, the Cochrane Library, the Web of Science, and Scopus by Redondo et al. (2018), Epstein et al. (2015), Dubois et al. (2019), and Kao et al. (2020) indicated that households with women earning a higher income and are decision-makers on food purchases tend to consume fewer SSBs [56,57,58,59]. However, studies reveal that the low-income quintile spend a large proportion of their income on the sugar tax [60]. Studies among university students across Africa found that the sweet taste is the main ingredient that attracts their intake [61,62,63]. The studies by Eykelenboom et al. (2022) and Thow et al. (2022) on stakeholder views on sugar tax point out that the tax is a sustainable political intervention for improving public health [64,65]. In recognition of the tax as a sustainable tool, Zambia, Kenya, South Africa, Mauritius, and many other African countries have implemented some form of excise taxes on non-alcoholic beverages [66,67].

3. Study Methodology

An Exploratory Sequential Design (ESD) mixed methods research was adopted. The formulation of both the structural interview questions and the questionnaire were adopted from Julia, Mejean, Vicari, Peneau, and Hercberg (2015); Rocke, Garib, Dalrymple, Nichols, and Ramcharitar-Boume (2016); Swift, Callahan, Cooper, and Parkin (2018); and Wyer and Xu (2010) [68,69,70,71]. Furthermore, the specific selection and formulation of the questionnaire was guided and informed by the gaps found in the qualitative data collection, analysis, and interpretation. The structural interview of the participants was conducted on 6 grocery shop owners or managers, 6 households/families, and 13 individuals at the residential areas of Gauteng province. Meanwhile, the questionnaire were administered to a minimum of 418 individuals according to the Krejcie and Morgan’s (970: 607–610) table of estimates [72] at shopping centres at Gauteng province. The table of estimates provides the researcher with clear numbers of the sample size in proportion to the study population. The convenience of the use of estimates from the table provides advantages over the choice of other sample formulae. The province was selected because of its inherently diverse nature, and it has the highest population of inhabitants. Approximately 11.18% of the household income is spent on food, beverages, and other consumables. The province is made of a mix of different races, cultural, social, and economic backgrounds, and various consumption habits or behaviours, which offers the prospect of a rich data collection site.
This study used mainly primary data collected from participants of Gauteng Province of South Africa. The qualitative semi-structured interview was conducted on 25 participants. Meanwhile the questionnaire was administered to a minimum of 418 participants [72]. The thematic analysis approach was used to categorise the qualitative data into six themes and sub-themes, and participants’ responses are coded as P1–P25 for confidentiality and anonymity purposes [73]. Factor extraction was performed to classify the quantitative variables into seven factors [74]. The confirmatory factor analysis (CFA) model was used to analyse the quantitative data.

4. Data Analysis

The six themes and sub-themes are presented in Figure 1 below. Theme 1 was developed to assess consumers’ knowledge on the causes of the changes in prices of SSB, the impact of prices on consumption, and consumer behavioural changes due to the introduction of the sugar tax. Theme 2 examined the effects of the sweet taste on consumption and the impact of the reformulation initiative on consumer behaviour patterns relating to SSB intake. Theme 3 examined the influence of advertisement on participants’ intake of SSBs, and their opinion of the impact of the sugar tax on production and sales of SSBs, as well as on employment within the sweetened beverage industry. Theme 4 analysed consumers’ perception and awareness of the sugar tax, while theme 5 examined the influence of consumer’s age, as well as social and household background, on the intake of SSBs. Finally, theme 6, evaluated the effectiveness of the current sugar tax by examining the sugar strategy, the reformulation initiative on intake, and the use of education of the public on the adverse effects of over-consumption of SSBs.
Theme 1 (Figure 1) assessed participants’ knowledge on the causes of increase in prices of SSBs and their consumption behaviour prior to and after the introduction of the sugar tax. Majority (92%) of the participants noticed the price increase but were unaware of the cause of the price increase. P13, P16, P18, and P3 (shop manager) acknowledged that the price of SSBs has increased. According to P1 (household), “The prices of energy drinks have increased because of increase in petrol and transport costs.” P18 stated that “The SSB have become expensive, but I do not know the reason for the price increase.” P13 said that “The prices of the SSB have doubled over the last three years, and I don’t understand the cause of the price increase.” According to P9 (household), “We consume fewer SSB now because the drinks have become more expensive.” P21 stated that “I spend less on the SSB, so price increase will not affect the quantity that I consume.”
From Figure 1, theme 2 examined the impact of sweet taste and reformulation on consumption of SSBs. The results found that the majority (88%) of participants reported that sweet taste was the main factor that influenced their intake of SSBs. In line with the study of Liem and Russell (2019), this study found taste to be attractive, irresistible, and addictive to majority of consumers [62]. P17 (household) indicated “We drink it because it is sweet, and the flavour tastes nice.”
According to P1 (household), “My children prefer ‘Oros’ (sweetened beverage); we mix one litre of the beverage with five litres of water because it is very sweet.” According to P4, “Sweet taste is the main ingredient that attracts our consumption.” P5 (household) explains that “I prefer to drink water but my children like SSB because of their sweet taste.” The initiative of the sugar tax to reduce excess sugar intake resulted in the reformulation of some beverages to reduce sugar content. These study results indicate that the majority of consumers dislike the taste of reformulated SSBs. For example, P3 (Shop owner) and P22 (Shop manager) pointed out that majority of customers do not purchase reformulated beverages; they prefer the original taste of Coke. Some participants, such as P17 (household), indicated that “Even though there are available drinks with reduced sugar content, we do find many other low-cost SSB high in sugar content.” P25 (household) stated that “We do not think reformulation will reduce intake; we don’t consume the zero-sugar beverages.”
Theme 3 analysed the effects of advertisement, production, sales, and job losses from the SSB sector. The results found that participants often searched in stores for specials (discount prices). According to P1 (household), “My children always ask their mother to purchase beverages advertised by celebrities on television.” P22 (shop manager) stated that “I get to see low prices of SSB and other food items from pamphlets in stores.” According to P24, “We are often enticed to purchase SSB by the in-store advertisement.” According to P23, P2, P25 (Household), P21, and P17 (Household), they usually search for and compare the prices of SSBs in the shops before purchasing those at very low prices. Some participants agreed that the use of a sugar tax would result in a decrease in production, sales, and loss of jobs in the SSB sector. P1 (household) reported that “Decreases in consumption will lead to a decrease in production and to loss of jobs.” P20 (shop owner) indicated that “Companies will lay off employees to maximise profit.” According to P17 (Household), “If the sugar tax rate becomes unbearable, consumption will fall, production will decrease, as well as employees will lose their jobs.” Proponents of the sugar tax, such as P8 and P3 (shop owner), pointed out that a decline in production and job losses would not occur, because alternative beverages with lower sugar content would be manufactured.
Theme 4 examined consumer perception, awareness, and the purpose of a sugar tax. The results found that 92% of participants are aware of the effects of over-consumption of SSB. According to P17 (household), “We were told at the clinic that over-consumption of SSB causes obesity, diabetes, heart diseases, and even high blood pressure.” P3 (shop owner) stated that “Energy drinks cause heartburn and increase heartbeat.” P1 (household) stated that “We are aware that the beverage causes sicknesses like kidney problems and diabetes.” Regarding the perception of the intake of energy SSB, most participants perceive high-caffeine SSB to provide an energy boost during physical activities, driving long distances, and staying awake for longer periods at night. According to P19, “Energy beverage intake increases my work performance due to its energy content.” P5 (household) stated that “We consume the beverage to enhance energy after consuming alcohol.” According to P3 (shop manager) and P14 (shop owner), “When we feel tired, we drink energy beverages to regain strength”. The results of the purpose of SSB tax found that, majority (64%) of participants reported revenue generation as the main purpose of the introduction of the sugar tax in South Africa. According to P13, “I think the main objective is to generate revenue, not to reduce consumption of SSB.” P18 stated that “The purpose of the sugar tax is to generate revenue, the government understands the sources that can generate massive taxes, for example, alcohol, tobacco, and SSB.” P2 stated that “Government needs money to create employment, so they tax every beverage including alcohol.”
Meanwhile, theme 5 and its related sub-themes reflect on the effects of consumer’s age, as well as social and economic backgrounds, on the intake of SSBs. The results of the age analysis revealed that 52% of participants of all ages consume two or more cans of any sort of sweetened beverages daily. According to P12 (age 18–25), “I consume less of SSB because my income is low.” P6 (age 18–25) stated that “Currently, I am consuming more energy beverages because I need the energy to concentrate at my workplace.” P1 (age over 46) (household) explained that “At this age (46), I need more energy, so I drink 2–3 cans of either Reboost, Score, or Monster energy drinks every day.” According to P18 (age 26–35), “I need energy to perform at the workplace, so I consume about two cans of Score or Monster beverage every day.” P20 (age 26–35) (shop owner) stated that “I consume about two cans of the beverage every week, and the quantity has not changed over the years.” Participants across all ages stated that they are willing to reduce their intake, and some have already reduced their consumption of SSB for health reasons, old age, or increased prices. For example, P24 (36–45), P11 (age over 46) (shop owner), P10 (age over 46), P17 (age over 46) (household), P8 (age 36–45), P3 (shop owner), P21 (age 36–45), P9 (household), P4, and P15 (36–45) (household) pointed out that they are aware that the over-consumption of SSB causes harmful effects to their health, and they have reduced their intake of the SSB.
The results regarding the consumers’ social and household influence on consumption revealed that 42% were influenced by their household consumption and 40% by social activities with friends. The majority (84%) of the participants who are influenced by household and social settings reported that the beverages are often consumed during family gatherings, celebrations, or during meals at home. According to P23, P2, and P22 (shop manager), they prefer the more expensive (quality) energy drinks (Red Bull and Energizer) when disposable income increases. According to P22 (shop manager), “From the day I found employment, we can now consume beverages after meals.” P14 (shop manager) stated that “SSB are the only drinks we can afford; sometimes we mix with alcohol to get a better taste.” P23 stated that “Now I consume more, unlike in high school when I did not have a job.” According to P3, “We do not have enough income to purchase the pure fruit juice, so we consume SSB.”
Theme 6 and its sub-themes evaluated the effectiveness of the current sugar tax and the use of education to reduce the intake of SSBs. Encouragingly, 66% of participants are in support of the sugar tax initiative and viewed the inclusion of education of the public to increase awareness of the purpose of the tax. According to P19, “Yes, I think sugar tax may result in reduced intake, especially to the low-income households.” P15 stated that “If the prices of SSB become more expensive, consumers will reduce intake.” P16 stated that “Government should increase the sugar tax rate to about 30% to force consumers to reduce intake.” P6 agreed that “Yes, I agree that sugar tax can reduce consumption, especially by the poor communities”. According to P10, “Sugar tax alone may not reduce intake to the level desired by the government, education of the public should also be considered.” P3 (shop owner) agreed that “Yes I think education can assist in reducing intake, but consumers need to satisfy their taste addiction and pure fruit juice is too expensive for many low-income consumers.” According to P24, “Government can educate the communities, but if pure fruit juice is still expensive, consumers will continue to purchase SSB.” P16 stated that “Yes, I agree that education of school children and the community members can reduce intake.”

5. The Principal Component Analysis

The principal component analysis (PCA) was used to analyse the quantitative data. The purpose of the PCA is to reduce the quantitative data and to explore the relationships among the variables in a data set [75]. However, there often exists a lesser inherent dimensionality in the data sets, where not all the variables are needed to convey the information relevant in consideration of the process. Eigenvalues examination (‘Guttman rule’) by Guttman (1954) and Kaiser (1960), known as (KG), was used to decide the number of variables and the “Guttman rule” was implemented to eliminate redundant items [76,77], and it requires researchers to retain eigenvalues greater than 1.0 [78,79]. According to Costello and Osborne (2005) eigenvalues greater than one are considered stronger and “significant” dimensions [79]. Consistent with the ‘Kaiser Criterion’ [80], all the initial eigenvalues in this data are greater than 1, as indicated in Table 1 below, and all the seven components were retained for interpretation.
Following the dimensional reduction of the original 30 variables, the confirmatory factor analysis (CFA) identified seven factors based on the eigenvalue criterion of values greater than one (1). The seven factors collectively explained an aggregate loading of 51.39% (Table 1) of the variance in confidence observed in the data. Researchers differ on how much variance should be explained before the number of factors is sufficient; some indicate that 50% of the variance explained is adequate and acceptable [81,82]. Table 2 below illustrates the initial factor matrix, where most of the factors contain less than the required minimum items of at least three indicators. For this reason, factor rotation was necessary to better the initial solution.

5.1. Factor Extraction

Factor extraction model indicates that observed measures are affected by underlying common and unique factors, and therefore the correlation patterns were determined. A summary of the descriptive statistics of the seven factors is shown in Table 3 below. The N values only include variables with no missing data. The minimum and maximum were within appropriate ranges for each variable. Descriptive statistics analysis was conducted using the mean, median, SD, skewness, and kurtosis on each of the identified factors. The absolute skewness and kurtosis values (all between −2 and 2) indicate that a normal distribution could be assumed for all of the seven factors identified.
All the means seemed reasonable, with a minimum of 6.8804 (±1.66788) and a maximum of 20.4067 (±5.04057) indicating that the majority of the participants contributed positively to the constructs in this study, and the standard deviation (SD) values display a narrow spread around the mean. The mean values display a range between 6.8804 and 20.4067 and the standard deviation indicates a minimum and maximum range between 1.66788 and 5.409924, signalling that the variables were somewhat appropriately classified into the relevant factors. The median values are positively and negatively skewed in varying factors. Where the median is greater than the mean, such as in factors 1, 3, 4, and 6, it indicates that the majority of the variables will be rejected and/or result in a negative outcome to the proposed relationship in the study. The reverse is true where the median values are lesser than the mean values, where majority of the proposed relationship of the variables may turn out to be positive.
In addition, a CFA procedure was conducted to assess all constructs involved in the study. The data represented the scores of 418 participants on seven factors influencing the consumption of SSBs in South Africa. The arrows from the factors to the variables represent linear regression coefficients or “factor loadings” [83,84] (Figure 2). The figure indicates that all the factors have associations or relationships that influence one another; for example, the ‘relevance of sugar tax’ (factor 7) points out that consumer’s knowledge of the relevance of the tax could lead to acceptance of the sugar tax and reduce intake of the beverages as a result of the negative behaviour and attitude towards the beverage intake, thus leading to an effective sugar tax implementation in South Africa. On a similar note, ‘the acceptance of the sugar tax’ (factor 6) is associated with reduced intake of SSBs as well as changes in attitude and behaviour toward SSB intake, thus leading to an effective sugar tax adoption.
Pearson’s correlation coefficient (r) was computed to evaluate the strength and direction of the linear relationships between the seven factors, namely SSB intake (factor 1), behaviour towards SSB intake (factor 2), attitude towards SSB intake (factor 3), effective sugar tax (factor 4), reduced intake of SSBs (factor 5), acceptance of sugar tax (factor 6), and relevance of sugar tax (factor 7). The correlations display r, and the statistically significance values represent sig or p. The results indicate that there is a weak positive correlation between SSB intake and reduced intake of SSBs (r = 193, p < 0.10), as well as relevance of sugar tax (r = 187, p < 0.010). The factor ‘behaviour towards SSB intake’ has a strong (significant) positive correlation with the factor ‘attitude towards SSB intake’ (r = 0.567, p < 0.010), a moderate positive correlation with ‘effective sugar tax’ (r = 0.343, p < 0.10), as well as weak positive correlation with the factors ‘acceptance of sugar tax’ (r = 0.137, p < 0.010) and ‘relevance of sugar tax’ (r = 0.218, p < 0.010). The results of the factor ‘attitude towards SSB intake’ presents a weak positive correlation between the factors ‘effective sugar tax’ (r = 0.266, p < 0.010), ‘reduce intake of SSB’ (r = 0.127, p < 0.010), ‘acceptance of sugar tax’ (r = 0.097, p < 0.010), and the ‘relevance of sugar tax’ (r = 0.173, p < 0.010). Meanwhile, the factor ‘reduce intake of SSB’ displays a weak positive correlation with the factor ‘acceptance of sugar tax (r = 0.120, p < 0.010), as well as a moderate positive correlation with the factor ‘relevance of sugar tax’ (r = 0.314, p < 0.010). These results indicate that consumer acceptance and the relevance of sugar tax has a positive influence in reducing intake of SSBs in South Africa. The outcome of the factor ‘acceptance of sugar tax’ indicates a weak positive correlation with the factor ‘relevance of sugar tax’ (r = 0.112, p < 0.010). This result reveals that consumer acceptance of the tax is positively relevant in influencing the success of sugar tax in reducing consumption of the SSB in South Africa.

5.2. Goodness-of-Fit Indices

The model demonstrated a satisfactory fit based on the comparative fit index (CFI) value of 0.9982, (see Table 4 below), exceeding the recommended threshold of 0.9 [85,86], and the root mean square error of approximation (RMSEA) value of 0.048, which was below the threshold of 0.07, indicating a high level of fit for the model.
The above table represents [85,86] suggested measures of model fit values. The model indicates that if a GFI = 0.90, AGFI = 0.80, NFI = 0.90, CFI = 0.90 and above, RMSR = 0.10, and RMSEA = 0.07 and less, these are within the acceptable limits. The model values in this study are deemed satisfactory, even with minor shortfalls. The Chi-square test statistic is not significant at 0.05, which suggests that the model fitting is only just acceptable. The result indicates that five determiners are ratio of Cmin/df, goodness-of-fit index (GFI), normed fit index (NFI), comparative fit index (CFI), and root mean square error of approximation (RMSEA). The model fit indices are all within specifications. All parameter estimates were significant at the 0.01 level. The p-value of the Chi-square value is satisfactory.
Therefore, Cmin/df is 1.979 (spec. < 2.0), GFI = 0.890 (spec. > 0.95), NFI = 0.740 (spec. > 0.95), CFI = 0.849 (spec. > 0.95), and RMSEA = 0.048 (spec. < 0.080). The root mean square error of approximation (RMSEA) is 0.03202 and since it is less than 0.05, it indicates a good fit. The Goodness-of-fit index (GFI) and Adjusted Goodness-of-fit index (AGFI) are larger than 0.9, which again reflects a good fit, although GFI and AGFI may not be as informative as the Chi-square test statistics and RMSEA. The model provided an acceptable fit for CFI (0.9982 > 0.9) and RMSEA (0.048 < 0.07). Therefore, the model was retained due to its simplicity, alignment with the observed data, and theoretical coherence.

6. Analysis of the Measurement Model

Several tests were conducted to determine whether a set of variables predicted a significant proportion of the variance in the consumption of SSB, and whether each variable accounted for a significant proportion of that variance independently. A main effects regression model was developed and tested all seven variables (Table 5).

7. Discussion of the Results of the Study

In these study results, we found that there is a highly significant association between the factors ‘SSB intake’ and ‘relevance of sugar tax’ (SSBI <--> RST) (y = 0.341). The association of the relationship states that consumer recognition of the relevance of the sugar tax is highly significant, with a maximum likelihood estimation of (0.341) in reducing SSB intake. This means that the relationship between these factors is relevant in contributing to the effective adoption of a sugar tax in South Africa. In addition, the review revealed that the study model supported the hypotheses that there were highly significant positive relationships between consumer ‘behaviour towards SSB intake’ and ‘attitude towards SSB intake’ (BTSSBI <--> ATSSBI) (y = 0.345), ‘behaviour towards SSB intake’ and ‘acceptance of sugar tax’ (BTSSBI <--> AST) (y = 0.394), and a significant positive association between ‘behaviour towards SSB intake’ and ‘effective sugar tax’ (BTSSBI <--> EST) (y = 0.153) (Table 4). These relationships state that consumer display of a negative attitude towards SSB intake and the acceptance of sugar tax is highly significant with a maximum likelihood estimation of 0.345 and 0.394 of resulting in reduction in intake of the SSB and maximum likelihood of 0.153 of effective sugar tax in South Africa.
A further review of the results found that there is a significant positive relationship between consumer ‘attitude towards SSB intake’ and ‘effective sugar tax’ (ATSSBI <--> EST) (y = 0.148), and a highly significant association between ‘attitude towards SSB intake’ and ‘acceptance of sugar tax’ (ATSSBI <--> AST) (y = 0.380) (Table 4). The relationships state that consumer display of a negative attitude towards SSB intake is highly significantly likely (0.380) to lead to acceptance of sugar tax, and significantly likelihood estimation of 0.148 to result in the effective implementation of the tax in South Africa. The relationship stems from the notion that, consumers specific display of a negative attitude towards the sweet taste and energy benefits from the SSB intake, is highly relevant in contributing to the effective adoption of the sugar tax in South Africa.
Furthermore, we found that there is a significant positive association between public ‘acceptance of sugar tax’ and the adoption of an ‘effective sugar tax’ (AST <--> EST) (y = 0.132) in South Africa. This means that the public acceptance of the sugar tax has a maximum likelihood estimation of 0.132 in leading to effective sugar tax implementation in South Africa. It further translates that the public acceptance of the tax can be achieved if manufacturers of SSB are penalised for not adequately reducing the sugar content in all their beverages. In addition, the result points out that there is highly significant positive relationship between the ‘acceptance of the sugar tax’ and the ‘relevance of sugar tax’ (AST <--> RST) (y = 0.326) (Table 5) in reducing intake of SSB in South Africa. These results illustrate that the public recognition of the relevance of sugar tax in reducing intake of SSBs has a maximum significant likelihood of 0.326 in leading to the acceptance of the sugar tax in South Africa.

8. Conclusions

The study points out that policymakers and proponents of a sugar tax should foster and enhance consumer displays of a negative attitude and behaviour towards SSB intake, recognition of the relevance of the tax in reducing intake, and the acceptance of the tax by the public to achieve a sustainable sugar tax in South Africa. It further reveals that low consumer knowledge, unawareness of the purpose of the sugar tax, increased prices of pure fruit juice, and the insignificant price increase in SSBs are a hindrance to effective sugar tax implementation. The findings suggest that a lack of consumer awareness is negatively impacting the sugar tax, hence the need to increase national awareness of the tax [44,45]. In addition, we found that majority of the participants are influenced by the sweet taste, household consumption, the frequent marketing of SSBs, and consumer anticipation of energy benefits from the beverages. To attain a sustainable reduction in intake, we suggest the banning of SSB advertisement and significant price increases. These findings are in line with those of [43], where they indicated that a significant price increase and the banning of the advertisement of taxed beverages resulted in decreased consumption in countries and jurisdictions that have introduced a sugar tax.
Overall, the results indicate that the intake by a majority of the participants of all ages has not changed significantly despite the implementation of the sugar tax in South Africa. This finding is contrary to that of Amoutzopoulos et al. (2020) in the UK, which found that a greater proportion of the adults (19 to 64 years) adhered to the recommended intake by the WHO [54]. Our findings suggest that a sugar tax alone is not sufficient and sustainable in reducing the consumption of SSB. Therefore, to attain a sustainable reduction in intake of SSBs, we suggest that governments undertake a mix of increasing education of consumers on the adverse effects of over-consumption of SSB, reiteration of the purpose and importance of a sugar tax, a significant increase in the sugar tax rate, the regulation of the marketing of SSBs, as well as the penalisation of beverage manufacturers who do reduce sugar content in all their beverages to the required level by the WHO.

9. Limitations

Some limitations encountered during the (qualitative and quantitative) data collection were the participants’ unwillingness to disclose their consumption behaviour to third parties. This resulted in delays for the researcher and the research assistants to recruit willing participants into the study. The collection of data (418 samples) from the different towns and cities from Gauteng province was time-consuming, which caused a delay during the study period. Furthermore, data was collected from Gauteng province only. This may limit the generalisability and almost certainly result in fewer statistically significant improvements than would have been the case if data were collected from more provinces. This study did not research on other factors that cause overweight; it was limited to SSB intake and the use of sugar tax in reducing its intake, overweight, and the impact of the relationships of the variables to an effective sugar tax.

Author Contributions

Conceptualization, R.O.E., Methodology, R.O.E., Formal analysis, R.O.E., Investigation, R.O.E., Data curation, R.O.E., Writing-original draft preparation, R.O.E., Supervision S.W.M. and F.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki, and approved by the Economic and Management Sciences Research Ethics Committee and SENATE Committee for Research Ethics of the North-West University.

Informed Consent Statement

Informed consent was obtained from all participants involved in this study.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Convergence of themes toward enhancing effective sugar tax adoption. (Enowkenwa, 2025 unpublished PhD thesis).
Figure 1. Convergence of themes toward enhancing effective sugar tax adoption. (Enowkenwa, 2025 unpublished PhD thesis).
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Figure 2. Confirmatory Factor Analysis (CFA) Model. (Enowkenwa, 2025 unpublished PhD thesis) (3 May 2025).
Figure 2. Confirmatory Factor Analysis (CFA) Model. (Enowkenwa, 2025 unpublished PhD thesis) (3 May 2025).
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Table 1. Eigenvalues and participants’ self-assessment of a sugar tax.
Table 1. Eigenvalues and participants’ self-assessment of a sugar tax.
Total Variance Explained
ComponentInitial EigenvaluesExtraction Sums of Squared LoadingsRotation Sums of Squared Loadings
Total% of
Variance		Cumulative %Total% of
Variance		Cumulative %Total% of
Variance		Cumulative %
14.78915.96315.9634.78915.96315.9632.7439.1429.142
23.08510.28226.2453.08510.28226.2452.5078.35817.500
32.2837.61133.8562.2837.61133.8562.4598.19825.698
41.5995.33039.1871.5995.33039.1872.3987.99533.693
51.2994.33143.5181.2994.33143.5182.2917.63741.330
61.2484.15947.6771.2484.15947.6771.5515.17146.501
71.1153.71651.3931.1153.71651.3931.4684.89251.393
(Enowkenwa, 2025 unpublished PhD thesis).
Table 2. Initial component matrix of participants’ self-assessment of a sugar tax.
Table 2. Initial component matrix of participants’ self-assessment of a sugar tax.
1234567
Sugar tax will reduce intake of SSB0.4790.037−0.006−0.196−0.1850.332−0.177
I am in support of sugar tax0.486−0.014−0.101−0.1390.404−0.065−0.283
Sugar tax is unfair to the low-income group−0.3130.170−0.0220.0840.4560.1460.315
Due to price increase, I will switch to intake of 100% fruit juice 0.4290.102−0.181−0.160−0.058−0.0480.490
I will reduce intake of SSB0.673−0.067−0.085−0.021−0.0930.0290.166
I will read the labelling on SSB before purchase0.6110.028−0.087−0.1300.064−0.2250.154
I can decline to consume SSB0.593−0.122−0.084−0.0800.0930.204−0.012
I can develop self-regulation skills0.601−0.170−0.029−0.1910.074−0.454−0.016
I can develop self-awareness skills0.638−0.097−0.066−0.1450.192−0.449−0.010
Energy drinks is a source of energy to me−0.0130.5940.181−0.238−0.0480.199−0.075
Energy drinks keep me awake longer at night−0.0670.5110.268−0.3120.0120.096−0.041
SSBs gives me satisfaction−0.0500.5590.222−0.124−0.109−0.211−0.055
It is the only drink I can afford−0.0500.572−0.1300.0950.0970.142−0.299
I consume because I see others do so0.1300.385−0.4690.295−0.2510.0080.099
How likely are you to reduce intake of SSB?0.606−0.058−0.021−0.164−0.1030.3460.152
How likely are you to stop intake of SSB?0.488−0.2040.0550.0350.0300.4540.242
How likely are you to educate others on SSB
intake?		0.5630.079−0.067−0.3250.0670.093−0.177
I can reduce if I earn more income0.3200.150−0.2730.0550.4620.176−0.384
I can stop if the price becomes unaffordable0.3390.087−0.215−0.1240.316−0.068−0.277
The sweet taste of the beverages−0.0500.5340.302−0.2750.1170.0070.283
The price of the beverages0.2240.5390.081−0.0620.191−0.0180.208
Energy benefits from the beverages0.0600.5410.299−0.093−0.101−0.0720.044
Influence from friends0.1690.438−0.5210.393−0.1030.1040.040
Influence from advertisements0.1050.556−0.1050.239−0.097−0.302−0.051
Influence from family0.1660.335−0.5420.291−0.023−0.0460.059
Regulate the marketing of SSB0.2590.1150.5060.175−0.2550.102−0.139
Increase the sugar tax rate0.390−0.0440.3170.300−0.380−0.0450.043
Educate parents and children on SSB intake0.470−0.0500.5320.3280.1970.037−0.099
Introduce Sugar Smart App campaign0.4260.0080.3930.4900.254−0.0450.036
Motivate manufacturers to reduce sugar in
beverages		0.4300.0570.3700.3970.1300.0130.010
(Enowkenwa, 2025 unpublished PhD thesis) (3 May 2025).
Table 3. Descriptive statistics of the seven factors identified.
Table 3. Descriptive statistics of the seven factors identified.
N (Valid)MeanMedianStd.
Deviation		SkewnessKurtosisMinimumMaximum
FACTOR141820.406721.00005.04057−0.4810.125630
FACTOR241818.51218.00005.40992−0.108−0.363630
FACTOR341813.674614.00003.89606−0.43−0.306420
FACTOR441819.332520.00003.94652−0.8950.894525
FACTOR541810.346910.00003.980980.2−0.616420
FACTOR64186.88047.00001.66788−0.2140.141210
FACTOR74189.28959.00002.86799−0.067−0.451315
(Enowkenwa, 2025 unpublished PhD thesis).
Table 4. Model fit summary for the measurement model.
Table 4. Model fit summary for the measurement model.
Fit IndexRecommended ValueModel
χ2Non-significant at p < 0.05760.110
Degrees of freedom (df)n/a465 − 81 = 384
χ2/df (Cmin/df)<2.01.979
Goodness-of-fit index (GFI)>0.900.890
Adjusted Goodness-of-fit index (AGFI)>0.800.867
Comparative fit index (CFI)>0.900.9982
Root means square residuals (RMSRs)<0.100.130
Root means square error of approximation (RMSEA)<0.070.048
Normed fit index (NFI)>0.900.740
Parsimony normed fit index (PNFI)>0.600.653
Source: Hair et al. (2006) [85] and Anderson and Gerbing (1988) [86].
Table 5. Maximum likelihood estimation (MLE) results.
Table 5. Maximum likelihood estimation (MLE) results.
HProposed RelationshipEstimateS.E.C.R.pStudy
Results
H02SSB intake<-->Behaviour towards SSB intake−0.0200.026−0.7600.447Rejected
SSB intake<-->Attitude towards SSB intake−0.0340.031−1.1040.270Rejected
SSB intake<-->Effective sugar tax−0.0310.0211.4510.147Rejected
SSB intake<-->Reduce intake of SSB0.0710.0282.5700.010Rejected
H08SSB intake<-->Relevance of sugar tax0.3410.0645.3060.000 ***Supported
H02SSB intake<-->Acceptance of sugar tax−0.0320.046−0.6980.485Rejected
H03Behaviour towards SSB intake<-->Attitude towards SSB intake0.3450.0576.0300.000 ***Supported
Behaviour towards SSB intake<-->Effective sugar tax0.1530.0334.6290.000 ***Supported
Behaviour towards SSB intake<-->Reduce intake of SSB0.0610.0282.1650.030Rejected
Behaviour towards SSB intake<-->Relevance of sugar tax−0.0610.054−1.1480.251Rejected
Behaviour towards SSB intake<-->Acceptance of sugar tax0.3940.0675.8730.000 ***Supported
H09Attitude towards SSB
intake		<-->Effective sugar tax0.1480.0344.3310.000 ***Supported
Attitude towards SSB intake<-->Reduce intake of SSB−0.0370.0321.1610.246Rejected
Attitude towards SSB intake<-->Relevance of sugar tax−0.1150.064−1.8040.071Rejected
Attitude towards SSB intake<-->Acceptance of sugar tax0.3800.0705.4540.000 ***Supported
H05Effective sugar tax<-->Reduce intake of SSB−0.0210.021−0.9830.326Rejected
H01Effective sugar tax<-->Relevance of sugar tax−0.0560.043−1.2880.198Rejected
H01Effective sugar tax<-->Acceptance of sugar tax0.1320.0433.0710.000 ***Supported
H10Acceptance of sugar tax<-->Relevance of sugar tax0.3260.0664.9380.000 ***Supported
H07Reduce intake of SSB<-->Acceptance of sugar tax0.0830.0491.7020.089Rejected
H04Relevance of sugar tax<-->Acceptance of sugar tax−0.1880.096−1.9500.051Rejected
Notes: *** p < 0.01; S.E. = standard error, S.V = 0.000, p-value or significant value. Source: (Enowkenwa, 2025 unpublished PhD thesis).
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Enowkenwa, R.O.; Musvoto, S.W.; Ganda, F. Examining the Sustainable Impact of the Relationship Among the Variables Influencing Sugar-Sweetened Beverage Intake on Sugar Tax. Sustainability 2025, 17, 7474. https://doi.org/10.3390/su17167474

AMA Style

Enowkenwa RO, Musvoto SW, Ganda F. Examining the Sustainable Impact of the Relationship Among the Variables Influencing Sugar-Sweetened Beverage Intake on Sugar Tax. Sustainability. 2025; 17(16):7474. https://doi.org/10.3390/su17167474

Chicago/Turabian Style

Enowkenwa, Rawlings Obenembot, Saratiel Wedzarai Musvoto, and Fortune Ganda. 2025. "Examining the Sustainable Impact of the Relationship Among the Variables Influencing Sugar-Sweetened Beverage Intake on Sugar Tax" Sustainability 17, no. 16: 7474. https://doi.org/10.3390/su17167474

APA Style

Enowkenwa, R. O., Musvoto, S. W., & Ganda, F. (2025). Examining the Sustainable Impact of the Relationship Among the Variables Influencing Sugar-Sweetened Beverage Intake on Sugar Tax. Sustainability, 17(16), 7474. https://doi.org/10.3390/su17167474

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