Globally, post-coronavirus disease 2019 (COVID-19) pandemic, online shopping has steadily increased with consumers becoming more comfortable in purchasing items online (Shaw, Eschenbrenner & Baier 2022). This has also been evident in South Africa, where, according to Statista (2025), the e-commerce sector is projected to reach $ 7.32 billion in 2025, with an annual growth rate of 9.49% predicted between 2025 and 2029. While all categories are expected to grow, the grocery category is expected to grow significantly. According to Ligaraba et al. (2023), shopping online for groceries consists of purchasing drinks, food, cleaning products, daily necessities and fast-moving consumer goods using an online platform like a mobile app. In South Africa, grocery retailers like Checkers, Pick n Pay, Woolworths and Spar have developed online shopping platforms that sell groceries online directly to end-user consumers (Musikavanhu & Musakuro 2023; Njomane & Telukdarie 2022).

Mobile applications (apps) have been found to be the most popular choice of platform, particularly among Generation X and Generation Y consumers who are currently driving the online grocery shopping industry in South Africa. This is because of the continued growth of smartphone adoption, which is propelled by increased mobile infrastructure, more affordable prices and declining data charges (Bruwer, Madinga & Bundwini 2022). In addition, behaviourally, according to Statista (2023), these cohorts are comfortable with using mobile apps to make purchases, and Maduku and Thusi (2023) add that Generation X and Generation Y consumers prefer shopping via apps because of the attractive discounts they can enjoy on their purchases. However, according to Arora, Malik and Chawla (2019), while these cohorts both use mobile apps more than other generational cohorts, they differ significantly in terms of their perceptions. This is supported by other authors (e.g. Moodley & Buthelezi 2023; Suhartanto et al. 2023) who argue the importance of understanding the consumer’s perceptions of e-service quality (e-SQ) across these cohorts. E-service quality is especially important as it facilitates the efficient and effective purchasing of products. However, the challenge arises in the factors that affect the e-SQ perceptions are expected to differ according to the cohorts because of their varied expectations (Suhartanto et al. 2023).

Although previous research on online grocery shopping exists and has addressed several issues – such as pre- and post-COVID-19 behaviour (Meister et al. 2023); underlying demands on online grocery shopping in the future (Eriksson & Stenius 2023); barriers to online grocery shopping among millennials (Jha 2024) and revealing the gaps, trends and major topics in online grocery shopping (Monoarfa et al. 2024) – these studies have not devoted attention to exploring the differences between the generational cohorts in an emerging country, such as South Africa, which offers lucrative opportunities for retailers. As Generation X and Generation Y consumers are the predominant cohorts shopping online for groceries, understanding what drives their e-SQ perceptions will allow theoretical and practical contributions. This study makes a key academic contribution by advancing understanding of how generational differences shape e-SQ perceptions in an emerging market, using South Africa’s growing online grocery sector as context. It extends Generational Cohort Theory (GCT), Expectation Disconfirmation Theory (EDT) and Online Relationship Marketing (ORM) by applying them in a digitally evolving African market. From a practical perspective, it offers valuable insights to online retailers by showing that Generation X values service convenience, while Generation Y prioritises platform content. This challenges universal digital strategies and emphasises the need for targeted approaches based on generational expectations. With South Africa’s e-commerce sector projected to reach $ 7.32 billion by 2025 (Statista 2025), these findings help local retailers better align their platforms with consumer needs to enhance satisfaction, trust and loyalty in a competitive environment. Therefore, the aim of this study is to investigate how e-SQ perceptions differ among generations (X and Y) within the South African retail grocery industry.

The paper next outlines the theoretical frameworks that underpin the research, followed by a detailed discussion of the proposed hypotheses and the conceptual model. Thereafter, the research methodology is explained, leading to an overview of the key findings and the study’s contributions. The section concludes with practical recommendations based on the results.

To support the aim of the study, the following objectives have been developed:

The study is grounded in three theoretical frameworks: generational cohort theory (GCT), online reputation management (ORM) and EDT. The concept of generational differences was introduced by social scientist Karl Mannheim in 1952, who described GCT as the idea that individuals born within a particular period or place perceive themselves as part of a cohort who share unique patterns and experiences that significantly shape their outlook on life. Expanding upon the Mannheim (1952) notion of age location, Strauss and Howe (1991) proposed that analysing how historical events influence the characteristics of different age groups provides insight into their development. Similarly, McKercher et al. (2020) suggested that a collective consciousness and shared values or beliefs among individuals of a specific age group contribute to their unified experiences. Generation X consists of individuals born between 1961 and 1979, whereas Generation Y comprises people born between 1980 and 1999 (Jang & Sung 2021; Lissitsa & Kol 2016, 2021). Because of age differences, Generation X tends to be more hesitant in embracing new technology, compared to younger cohorts like Generation Y. This reluctance stems from Generation X’s introduction to the digital realm during its adulthood stages, which reinforces their inclination towards privacy (Arachchi & Samarasinghe 2024). In a study conducted by Zhuang et al. (2021), focusing on food delivery apps, Generation X and Generation Y consumers differed in their perceptions of e-SQ when age was used as a moderator towards overall service quality. This highlights the role that GCT plays in understanding and explaining the distinctions in online shopping behaviour between Generation X and Generation Y consumers (Arachchi & Samarasinghe 2024).

Online reputation management originates from traditional relationship marketing theory established by Berry in the early 1980s, concentrating on cultivating and sustaining long-term relationships within an online setting (Hunt, Arnett & Madhavaram 2006:74). Utilised as a strategic approach, ORM aims to cultivate consumer e-trust, enhance experiences and boost e-satisfaction, thereby fostering profitable relationships and enhancing e-SQ (Boateng 2019; Thaichon et al. 2020). This notion is reinforced by Steinhoff et al. (2019), who asserted that seamlessly providing online services reduces uncertainty and enhances the establishment of positive online relationships with consumers. The adept application of ORM theory is indispensable for enhancing business performance and forging enduring consumer relationships (Boateng 2019:227). This is supported by Ribbink et al. (2020), who indicate that through the management of online relationships, retailers can directly influence loyalty indirectly by ensuring satisfaction.

Finally, EDT (Oliver 1977, 1980) has been instrumental in shaping much of marketing literature in terms of exploring consumer satisfaction. According to Oliver (1977), consumers enter interactions with firms’ products or services with preconceived expectations. After purchasing a product or service, consumers then form perceptions based on its actual performance (Sinha et al. 2020). Spreng and Mackoy (1996) further explained that if a product or service surpasses a customer’s initial expectations, it creates a positive disconfirmation, leading to satisfaction after purchase. Conversely, if it falls short of expectations, it results in negative disconfirmation, reducing post-purchase satisfaction. Essentially, EDT centres on assessing perceived expectations, performance and the resultant levels of satisfaction or dissatisfaction (Wong, Huang & Lin 2024). Within an e-SQ context, different e-SQ dimensions (such as perceived risk, platform content, ease-of-use and service convenience) can act as the basis for expectation (AlSokkar et al. 2024). This is confirmed by Liao et al. (2017), who state that consumers compare their perceptions of e-SQ after experiencing the service and determine whether they have positive or negative perceptions of the e-SQ received, which, ultimately, impacts their satisfaction.

Therefore, within the context of this study, the EDT is utilised to evaluate the e-SQ dimensions and whether the dimensions lead to positive perceptions of e-SQ, as supported by AlSokkar et al. (2024). This is important for online retailers to understand because of online retailers striving to build relationships with consumers to secure enduring customer relationships by securing satisfaction (Ribbink et al. 2020) through the ORM framework. Lastly, the GCT provides support for evaluating whether differences in e-SQ exist according to age, as supported by Zhuang et al. (2021).

This study is guided by three key theoretical frameworks: ORM, EDT and GCT. Each framework contributes uniquely to understanding the differences in e-SQ perceptions between Generation X and Generation Y consumers within South Africa’s growing online grocery market. For example, ORM is crucial as it emphasises building long-term relationships with consumers in digital environments (Thaichon et al. 2020). Post-pandemic, where online grocery shopping via mobile apps has become the norm (Shaw et al. 2022), ORM theory helps explain how factors such as platform content, ease of use and service convenience foster e-trust and satisfaction. As digital touchpoints replace physical interactions, ORM offers a lens through which to explore how service elements influence consumers’ perceptions of e-SQ and their continued engagement with online platforms (Boateng 2019; Butt & Umair 2023).

Expectation Disconfirmation Theory, originally developed by Oliver (1977), is equally relevant as it provides a framework for assessing customer satisfaction by comparing expectations with perceived performance. In this context, e-SQ dimensions such as perceived risk or platform content serve as expectation anchors. If the experience exceeds expectations, satisfaction is achieved; if not, dissatisfaction occurs (Hien et al. 2024). Expectation Disconfirmation Theory allows this study to measure whether different e-SQ dimensions produce positive or negative disconfirmation among the generational cohorts. Lastly, GCT helps segment consumers based on their shared experiences and values shaped during formative years. Generational traits impact how consumers perceive and interact with technology (Omoniyi et al. 2025). In this study, GCT supports the hypothesis that Generation X and Generation Y perceive online grocery experiences differently, necessitating tailored e-SQ strategies for each cohort.

Together, these theories offer a comprehensive framework where ORM explains the relational and strategic impact of online service elements, EDT evaluates satisfaction outcomes, and GCT highlights demographic differences. This integrated approach enables a deeper understanding of the drivers behind e-SQ perceptions among South Africa’s most active online grocery shoppers, thereby grounding the problem statement and hypotheses effectively.

Globalisation, greatly spurred by the information technology era, has fundamentally transformed how businesses cater to their consumers. Demir et al. (2021) explained that e-retail offers consumers an unprecedented array of shopping options, with e-SQ emerging as a key distinguishing factor. To stay competitive in an online environment, retailers must grasp the significance of e-SQ and its underlying factors, as it profoundly influences consumer segmentation and decision-making (Mamakou, Zaharias & Milesi 2024). Previous studies indicate that the relationships between e-SQ and its antecedents, such as perceived risk, platform content, ease of use and service convenience, impact consumers’ evaluations of their online shopping experiences, making it important to evaluate (Arilaha, Fahri & Buamonabot 2021; Saha et al. 2023; Syah & Olivia 2022; Udo, Bagchi & Kirs 2010).

The main purpose of this study was to investigate how e-SQ perceptions among different generational cohorts (X and Y) vary in the South African online grocery industry. A self-administered online survey was employed across the Gauteng province of South Africa. Before undertaking the survey, the target population was clearly defined, which consisted of Generation X (born between 1961 and 1979) and Generation Y (born between 1980 and 1999; Jang & Sung 2021) who had purchased groceries online via the mobile apps using Checkers 60, SPAR2U, Woolies Dash or Pick n Pay asap! within the past three months. The data were collected using convenience sampling through a reputable research agency that had a panel of potential respondents who opted in to participating in research studies.

Ethical clearance to conduct this study was obtained from the University of Johannesburg and CBE Research Ethics Committee (No. 2023SCiiS018).

The questionnaire was pre-tested to ensure that the questionnaire was clear and unambiguous. The pre-test was conducted on 20 respondents per cohort, and there were minor errors identified, such as a coding error and the positioning of certain images within the questionnaire. Once corrections were made, the questionnaires were sent to respondents. Electronic consent was obtained from the respondents, and they were informed about their voluntary participation, the confidentiality and privacy of their personal information and their ability to withdraw from the online survey without any risks or harm.

A questionnaire was used to collect the study’s demographic information, and all the constructs’ items were on a five-point Likert-type scale. The scales were adopted from prior research studies: perceived risk factors (financial risk, product risk, time risk, delivery risk, social risk, security risk and psychological risk consisting of 27 items) were adapted from Ariffin, Mohan and Goh (2018). The platform content construct, consisting of five items, and the service convenience construct, consisting of four items, were adapted from Udo et al. (2010). The ease-of-use construct, consisting of four items, was adapted from Tabaeeian et al. (2023). The 18 items measuring e-SQ were adapted from the study conducted by Zia et al. (2023). When adapting the items, they were adapted by contextualising the items to the context of this study, focusing on online grocery apps.

The research sample for this study was derived from a heterogeneous population of Generation X and Generation Y consumers who had purchased groceries online via mobile apps. Potential respondents were sent emails inviting them to partake in the survey, with an embedded URL link leading them to the survey platform. Initially, respondents were asked three screening questions, having to confirm whether they resided in Gauteng, if they were born between 1961 and 1999 (belonging to Generation X and Generation Y cohorts) and if they had purchased groceries using mobile apps, such as Checkers Sixty60, Woolies Dash app, Pick n Pay asap! or SPAR2U. Respondents had to answer ‘yes’ to all three screening questions to proceed to the next phase of the questionnaire. To avoid non-response bias, the aim of this study was to obtain 600 responses, evenly split between Generation X (50%) and Generation Y (50%) consumers. However, the sample exceeded expectations, totalling 622 questionnaires, with 295 (47%) responses successfully collected from Generation X and 327 (53%) from Generation Y.

SmartPLS 4.0 was utilised to analyse the study’s data. Firstly, the measurement model was used to assess the reliability and validity of each construct presented in Figure 1. Secondly, a structural model was analysed to examine the proposed hypotheses developed for the study.

In this study, reliability and validity techniques were used to assess the measurement model’s suitability. To examine reliability, composite reliability (CR) and Cronbach’s alpha were assessed. A threshold of 0.7 or above is deemed satisfactory for both Cronbach’s alpha and CR, as recommended by Hair et al. (2022) and Page, Higgins and Sterne (2019). The results revealed that the Cronbach’s alpha values for various constructs examined in the study were above 0.7, suggesting a satisfactory level of internal consistency. Specifically, financial risk had a Cronbach’s alpha score of 0.729 and a CR score of 0.729, product risk had a Cronbach’s alpha score of 0.848 and a CR score was 0.848, time risk had a Cronbach’s alpha score of 0.869 and a CR score was 0.871, delivery risk had a Cronbach’s alpha score of 0.877 and a CR score of 0.887, social risk had a Cronbach’s alpha score of 0.888 and a CR score of 0.889, security risk had a Cronbach’s alpha score of 0.863 and a CR score of 0.869, psychological risk had a Cronbach’s alpha score of 0.820 and a CR score 0.821, platform content had a Cronbach’s alpha score of 0.868 and a CR score of 0.880, ease of use had a Cronbach’s alpha score of 0.897 and a CR score of 0.898, service convenience had a Cronbach’s alpha score and CR score of 0.901 and e-SQ had a Cronbach alpha’s score of 0.949 and a CR score of 0.952 (refer Table 1).

To evaluate the hypotheses, the regression coefficient (β) was utilised to gauge the strength of the relationships, while the p-value was utilised to ascertain the significance of these relationships. Statistical significance was determined using bootstrap resampling with 5000 subsamples at the 5% significance level. The path coefficients (β) and significance levels for Generation X and Generation Y are shown in Table 3 and Table 4, respectively. However, before hypothesis testing, multicollinearity was assessed using the variance inflation factor (VIF) values. All VIF values were below the threshold of 5.0 as recommended by Hair et al. (2022), ranging from 1.205 to 2.007 (perceived risk = 1.205, platform content = 1.781, ease of use = 2.007 and service convenience = 2.002) for Generation X and ranging from 1.218 to 2.722 (perceived risk = 1.218, platform content = 1.634, ease of use = 2.722 and service convenience = 2.309) for Generation Y.

According to Table 3, in terms of Generation X, perceived risk has a negative (β = −0.109) and small (f² ≤ 0.15) significant (p < 0.05) effect on e-SQ. A similar result was found for Generation Y (refer to Table 3), whereby perceived risk had a negative (β = −0.088) but small (f² ≤ 0.15) significant (p < 0.05) effect on e-SQ. For Generation X, platform content was found to have a positive (β = 0.215) but small (f² ≤ 0.15) significant (p < 0.05) effect on e-SQ. For Generation Y, as shown in Table 4, platform content has a positive (β = 0.313) but medium (f² ≤ 0.15) significant (p < 0.05) effect on e-SQ. However, the relationship for Generation Y was found to have a stronger effect compared to Generation X. Ease of use for Generation X consumers was found to have a positive (β = 0.271) but small (f² ≤ 0.15) significant (p < 0.05) effect on e-SQ. This effect was stronger for Generation X consumers, compared to Generation Y, as ease of use was found to have a positive (β = 0.308) but small (f² ≤ 0.15) significant (p < 0.05) effect on e-SQ. Lastly, service convenience was found to have a positive (β = 0.379), medium (f² ≥ 0.15) and significant (p < 0.05) effect on e-SQ for Generation X consumers, yet a positive (β = 0.253) but small (f² ≤ 0.15) significant (p < 0.05) effect on e-SQ for Generation Y consumers. Therefore, in summary, the strongest relationship and effect on e-SQ for Generation X was service convenience, while the strongest relationship and effect on e-SQ for Generation Y was platform content – as shown in Table 3 and Table 4. The structural model demonstrated substantial explanatory power for both cohorts. For Generation X, the model explained 65% of the variance in e-SQ (R² = 0.65), and for Generation Y, the model explained 63% of the variance in e-SQ (R² = 0.63). According to Hair et al. (2022), these values explain substantial explanatory power as they are greater than 0.50 demonstrating that the proposed model explains e-SQ perceptions adequately across both cohorts.

Based on the study’s findings, online grocery retailers should aim to implement prioritised, generation-specific approaches to enhancing e-SQ. Firstly, the priority should be optimising platform content, particularly for Generation Y consumers, as this was found to be the strongest factor influencing e-SQ. Therefore, retailers should prioritise improving the quality and richness of the content presented on their mobile apps. Generation Y consumers place a high value on appealing visuals, detailed product descriptions and authentic peer reviews. To address this need of the Generation Y cohort, grocery apps must ensure that product listings include high-resolution images, comprehensive nutritional information and user-generated content such as ratings and written reviews. Dynamic content updates and personalisation features that reflect browsing history and past purchases will further enhance engagement and increase perceptions of relevance and reliability, particularly among younger consumers. Although platform content was not as important for Generation X consumers, online grocery retailers should ensure that the content remains factual and clearly organised.

Secondly, the priority relates to service convenience, with a particular focus on Generation X, as this was found to be the strongest factor influencing e-SQ. This group values the practicality and time-saving potential that online grocery apps offer. To meet their expectations, retailers should focus on improving service efficiency by offering flexible delivery slots, accurate delivery tracking and responsive customer support. Features such as scheduled deliveries, real-time driver updates and the ability to edit or cancel orders after checkout can significantly enhance convenience. Additionally, clear communication channels and short response times in resolving queries or complaints are vital to maintaining the trust and satisfaction of this cohort.

Thirdly, the priority focuses on perceived risk, where a unified approach is required across both cohorts. Given that perceived risk negatively influences e-SQ, although minimally, it is imperative for retailers to minimise these risks to build consumer trust. This can be achieved by reinforcing the security of mobile applications through visible and reliable security features such as secure payment methods, encryption and privacy guarantees. Furthermore, transparency regarding refund and return policies, as well as consistent communication on order status and delivery timelines, can reduce uncertainty and improve customer confidence, thereby lessening the impact of perceived risk on e-SQ.

Fourthly, ease of use, while only minimally significant, remains a critical factor for both generational cohorts and demonstrates a slightly stronger influence for Generation Y. This finding suggests the need for intuitive app designs that facilitate seamless navigation and transaction processes. As such, retailers should ensure that their platforms offer user-friendly interfaces, streamlined shopping journeys and accessible features such as saved shopping lists, product search filters and quick reorder options. Generation Y, accustomed to digital interfaces, expects technology that is not only functional but also frictionless. Simplifying registration processes, enabling one-click purchases and integrating chatbot assistance can contribute positively to perceptions of ease of use across both groups but particularly for the younger cohort.

Fifthly, given the generational differences in e-SQ drivers, retailers should also consider segmenting their strategies to deliver tailored experiences that align with each group’s preferences. For Generation Y, who are more tech-savvy and visually driven, the focus should be on immersive, customisable and interactive app features that reflect a modern digital lifestyle. For Generation X, who value efficiency and reliability, emphasis should be placed on consistent service delivery and simplified processes that save time and reduce friction. By aligning platform strategies with the specific expectations of Generation X and Generation Y consumers, online grocery retailers in South Africa can significantly enhance perceived e-SQ, foster long-term customer satisfaction and strengthen their competitive advantage in the growing e-commerce market.

However, the study has its limitations, having focused on the Gauteng region and no other provinces in South Africa – this may be an opportunity for further research where additional provinces can be included to allow for a comparative analysis. In addition, this study centres on Generation X and Generation Y consumers, while other cohorts like baby boomers and Generation Alpha may also demonstrate differences in their perceptions of e-SQ. Lastly, the study only focuses on e-SQ as the outcome variable, and although e-SQ is important to understand and influence, additional aspects like customer satisfaction and loyalty may also be prevalent and perceptions of differences could be evident.