Background: Unlike digital natives, elderly consumers in emerging markets are slower to adopt mobile grocery shopping services. Current research on mobile grocery shopping in emerging markets does not elucidate the elderly’s intention to use mobile grocery shopping.

Aim: To address the literature gap, this study leverages the UTAUT2 to explore the determinants behind South African elderly consumers’ intention to use mobile grocery shopping services.

Method: A cross-sectional, non-probability research design was used to collect data from 269 elderly (65 years and older) grocery shoppers. SmartPLS version 4.1.0.7 was used to test the hypotheses.

Results: The in-sample predictive accuracy of the model was high (76%). Effort expectancy significantly influenced behavioural intention, while performance expectancy did not. Hedonic motivation is the second strongest influence on the intention to use mobile grocery shopping services, followed by price-value, social influence and habit. The moderation analysis revealed that the elderly are a diverse demographic with varied behaviours towards mobile grocery shopping. Notably, mobile shopping habits impact elderly males differently than females. In addition, the effects of hedonic motivation and effort expectancy on intention vary with the elderly’s age.

Conclusion: A specific set of UTAUT2 variables influences the elderly’s intention to use mobile grocery shopping services. The research shows that the elderly are heterogeneous in mobile shopping adoption behaviour.

Contribution: This study contributes to the limited research on the elderly’s intention to use mobile grocery shopping services in emerging markets.

Keywords: elderly consumers; grocery shopping; emerging markets; South Africa; UTAUT2; demographic differences.

Introduction

One of the most mundane activities for grown-ups is grocery shopping (Singh & Söderlund 2020). To make grocery shopping more convenient and fun for shoppers, mobile grocery shopping apps have become a pivotal strategy for retailers to compete in the multi-trillion-dollar grocery industry. Mobile grocery shopping apps offer various advantages to shoppers, such as portability, mobility, personalisation and ubiquity (Kumar, Chakraborty & Bala 2023).

Younger consumers dominate mobile grocery shopping services, while fewer older consumers use them (Hoh et al. 2023). This divide in mobile grocery shopping application usage is notable in South Africa (Mastercard 2023). However, across the world, the elderly segment presents good business opportunities in terms of size, wealth and consumption patterns, and businesses should proactively rethink strategies to fit this market segment (Chaouali & Souiden 2019). Thus, even in an emerging market country such as South Africa, attracting more elderly consumers to mobile grocery shopping services could assist retailers in recouping the costs of developing and maintaining mobile grocery shopping services, and tapping into the wealth of this niche segment.

Prior research on mobile grocery shopping in emerging markets made valuable contributions by mainly conducting research among the general population of a country (e.g. Chakraborty 2023; Hoh et al. 2023; Kim 2021). However, few respondents in these samples represented the elderly, defined as individuals 65 years and older (Berg & Liljedal 2022). The objective of this study is to investigate the determinants of elderly South African consumers’ intention to adopt mobile grocery shopping services, using one of the most cited technology adoption theories for research in a variety of settings and populations – the unified theory of acceptance and use of technology 2 (UTAUT2) framework (Blut et al. 2021). Such a study will contribute to the theoretical understanding of the elderly’s perceptions of mobile grocery shopping services, influencing their intention to use them.

The value of the study is as follows. The findings of this study add vital insights, filling gaps in knowledge and serving as a basis for future-related research in emerging markets. A fundamental task in developing a marketing strategy is the identification of meaningful sources of heterogeneity within a market, which enables the design of differentiated and segment-specific approaches to effectively engage diverse consumer groups. It is believed that the elderly as a market segment may not be homogenous in behaviour when using mobile services (Arenas-Gaitán, Villarejo Ramos & Peral-Peral 2020). Therefore, from a theory-building perspective and developing meaningful insights for retailers, it is essential to explore moderating variables that may highlight differences among elderly consumers regarding their intentions to use mobile grocery shopping services.

The manuscript is organised as follows. It begins with a review of mobile grocery shopping research in emerging markets to position the study in the existing literature. Next, it discusses the UTAUT2 model and formulates hypotheses based on its tenets and elderly consumer literature. Following this, the research methodology and data analysis plan are outlined. The main results of the hypothesis testing are presented, followed by a discussion of these findings. Finally, the manuscript concludes with the study’s theoretical contributions, managerial implications, limitations and suggestions for future research.

Prior research on mobile grocery shopping in emerging markets

Table 1 presents an overview of research published in academic journals on adopting mobile grocery shopping services in emerging markets.

The key theories used in prior research are the technology acceptance model (Al Amin et al. 2021; Bruwer, Madinga & Bundwini 2022; Pasupuleti, Jeyavelu & Seshadri 2021; Yuuiarty et al. 2020; Shukla & Sharma 2018), theory of planned behaviour (Kim & Moon 2023; Shukla & Sharma 2018) and the stimulus-organism-response model (Chakraborty 2023; Ligaraba et al. 2023). Other theories used in prior research include the valence theory (Hoh et al. 2023) and the uses and gratifications theory (Al Amin et al. 2021).

In Table 1, prior research primarily focused on collecting data from the general population in Malaysia, South Africa, India and South Korea, resulting in only a few respondents being elderly shoppers (65 years old and older). Also, some of the studies in Table 1 (Al Amin et al. 2021; Kim 2021; Pasupuleti et al. 2021) do not include any respondents who met the criteria for being elderly.

In conclusion, previous research on mobile grocery shopping in emerging markets has primarily focused on the general population, neglecting to investigate elderly consumers’ adoption of such a service. Notably, the summary in Table 1 does not include a UTAUT2 study. According to Tamilmani et al. (2021b), the UTAUT2 has two unique characteristics that make it more suitable than other frameworks for the study. The UTAUT2 has high predictive validity, around 74% of behavioural intention. Also, compared to the TAM and TPB, the UTAUT2 focuses on adopting new technologies by incorporating intrinsic motivation. Thus, using UTAUT2 to investigate the intention of the elderly to adopt mobile grocery shop services in the emerging market context would add valuable insights to close the gap in the literature.

Model development

The theoretical model used to achieve the objective of the study is shown in Figure 1. The model is based on the UTAUT2 framework, which was proven valid for studying consumer technology adoption in emerging markets (e.g. Malekpour et al. 2025). Tan and Ooi (2018) asserted that facilitation conditions become irrelevant in mobile shopping intention because of prior mobile device experience. In the context of this study, elderly mobile grocery shopping users must be smartphone users to use mobile grocery shopping services. Being smartphone users, the elderly would also have experience in using mobile apps. Therefore, facilitating conditions were excluded as a variable from the conceptual model of the study. Lastly, age and gender were included as moderators based on the recommendation by Blut et al. (2021) that UTAUT studies should, at a minimum, include the two moderators.

Performance expectancy influencing behavioural intention

In line with Venkatesh et al. (2003), performance expectancy was defined in this study as an elderly individual’s belief that using a mobile grocery shopping service would be helpful for purchasing groceries. Grocery shopping is a high-frequency functionalistic activity driven by individuals’ need to replenish daily household provisions. Using mobile shopping services enables individuals to do grocery shopping from anywhere and save time by not having to drive to a local store (Hoh et al. 2023). In this way, mobile grocery shopping supports the utilitarian aspect of grocery shopping, which could be an essential consideration for elderly shoppers in emerging markets:

Effort expectancy influencing behavioural intention

In the study, effort expectancy referred to the ease of use of mobile grocery shopping services. Older people can experience vision impairments, such as decreased visual capacity, reduced acuity, lower ability to distinguish contrast, increased sensitivity to glare, reduced sensitivity to colour and difficulty focusing on consecutive images (Laukkanen et al. 2007). The elderly can also suffer from fine motor skills impairment and arthritis that increases with age (Perez et al. 2023). The physiological changes associated with ageing could significantly influence the elderly’s ability to use mobile services easily (Özsungur 2022). Consequently, the expected effort in using mobile grocery shopping services could be an important factor influencing the elderly’s intention to adopt such technology in an emerging market country:

Social influence affecting behavioural intention

Social influence is ‘the extent to which consumers perceive that important others (e.g. family and friends) believe they should use a particular technology’ (Venkatesh, Thong & Xu 2012:159). Social influence can be vital in influencing the elderly’s intention to shop online (Roy, Basu & Ray 2023). In addition, the recent meta-analysis study by Bommer, Milevoj and Rana (2023) on the adoption of cryptocurrency in emerging markets showed that social influence statistically significantly influences the use of cryptocurrency. Therefore, it is proposed that the ‘social influence – intention’ relationship will hold in this study:

Price-value influencing use intention

The perceived price-value of mobile grocery shopping services refers to the cognitive trade-offs an elderly consumer makes between the perceived benefits of using a mobile grocery shopping service and the cost of using the service (Venkatesh et al. 2012). Shopping apps are free, and data costs have recently decreased (Chopdar & Sivakumar 2019). Thus, the leading mobile grocery shopping cost remaining is the delivery cost. In emerging markets, price-value is more important in customer decision-making for mass-produced products than in developed countries (Osburg et al. 2024). Thus, we expect in this study that price-value will influence mobile grocery shopping intention positively:

Hedonic motivation influencing behavioural intention

Drawing on Venkatesh et al. (2012), hedonic motivation was defined for the study as the fun and pleasure an elderly consumer would derive from using a mobile grocery shopping service. Even for the elderly, using new technology is determined by utilitarian and hedonic influences (Franco 2023). The assertion is supported by the meta-analysis study by Tamilmani et al. (2019), which shows that hedonic motivation positively influences behavioural intention for older consumers, as predicted in the UTAUT2:

Mobile shopping habit influencing behavioural intention

A recognised definition of habit is ‘Habits are learned sequences of acts that have become automatic responses to specific cues, and are functional in obtaining certain goals or end-states’ (Verplanken & Aarts 1999:104). This definition highlights three critical aspects of habits. Habit forms from prior behaviour. Habitual behaviour is an automatic response, and habitual behaviour leads to satisfactory outcomes.

According to De Guinea and Markus (2009), people can learn how to apply known features in entirely new sequences and contexts, or even ‘learn’ how to use entirely new features through unconscious generalisation because of environmental priming. Bezirgani and Lachapelle (2021) also found that the online shopping habit for non-grocery items influences the intention to purchase groceries online. Thus, it can be reasoned that elderly consumers’ general habit of using mobile shopping services could enhance their intention to use mobile grocery shopping services:

Age as a moderator

As explained in this article, cognitive functions decline with age, leading to vision impairments and increased difficulty with fine motor skills. Such physiological changes also lower the elderly’s susceptibility to the novelty of new technology as they prioritise ease of use, user-friendliness and mobile services that focus on their needs (Feng & Ivanov 2023; Iancu & Iancu 2020). The decline in cognitive abilities with ageing could also lead to the elderly facing difficulties in understanding and evaluating new technologies’ advantages (Charness & Boot 2009). Elderly consumers, because of cognitive ageing, may also rely more on habit as they are less likely to seek additional information and instead rely on heuristic or schema-based forms of processing when making decisions or solving problems (Fang et al. 2016). Because of spousal loss, the elderly rely heavily on family members, close friends and acquaintances from organisations such as the senior centre staff as significant influencers in their inner circles and prioritise close social relationships with them (Joa & Magsamen-Conrad 2022). Elderly consumers also face financial constraints as most are retired and receive a fixed income (Yap, Tan & Choon 2022). Consequently, the elderly might trivialise the benefits of mobile grocery shopping and exaggerate the delivery cost to save money on the grocery shopping process – classical cognitive dissonance elimination behaviour proposed by Festinger (1957). Thus, price-value might become a more influential variable as the elderly age.

Based on the discussion in this section, the following moderation hypotheses were formulated:

Gender as a moderator

Generally, men are more likely to shop online than females (Rahman et al. 2022). This gender difference can be explained by the fact that men tend to have significantly more positive perceptions than women about the compatibility of online shopping, complexity, relative advantage, result demonstrability and trust (Lian & Yen 2014). Compared to men, females are more influenced by the opinions of others than males (Özsungur 2022). Females also traditionally take responsibility for shopping and consider it an important task (Kempf, Palan & Laczniak 1997). They maximise the utility they get from the shopping process by reducing costs and increasing the benefits of grocery shopping (Mehta 2020). In contrast, men generally do not find shopping fulfilling as they prioritise achieving their goals and being in control (Arnold & Reynolds 2012). It has also been reported that there is a prevailing belief that females tend to engage with and enjoy computers and information technology less than men. This perception is often attributed to females displaying lower levels of self-efficacy and experiencing higher levels of computer anxiety (Koivisto & Hamari 2014).

Based on the gender differences discussed above, the following moderation hypotheses were formulated:

In summary, drawing on the UTUAT2 framework, it was empirically tested whether performance expectancy, effort expectancy, social influence, price-value, hedonic motivation and mobile shopping habit positively influence the elderly’s intention to use mobile grocery shopping services. In addition, the moderating effects of age and gender on the relationships between the independent variables and behavioural intention were also investigated.

Methodology

To address the study’s objective, a non-probability sampling procedure was used to collect data from 269 elderly consumers who own a smartphone and live in five retirement complexes in two South African cities. All questionnaires were screened for completeness and consistency. No cases were removed because of missing data. The retirement complex manager distributed the questionnaires to all residents who were willing to participate in the study. Sixty-five is the most used threshold age in marketing research on the elderly (Berg & Liljedal 2022). Therefore, all respondents had to be 65 years or older. Forty-three per cent of the respondents were males. Most male respondents had a monthly spendable income of R10 001–R15 000 (24.8%), made two shopping trips monthly (38.8%) and spent R501–R1500 per trip (73.6% = 36.8% + 36.8%). Most female respondents also had a monthly spendable income of R10 001–R15 000 (33.6%), made four or more shopping trips per month (53.0%) and spent R501–R1000 per trip (42.1%).

Data were collected using hard-copy questionnaires distributed by the management offices in the retirement complexes. This data-collecting process is similar to the study of Kelfve et al. (2020). The multi-item scale used to measure each variable in the conceptual model was adapted from previous studies. The measurement model included 29 items across seven constructs. Performance expectancy was measured using five items developed by Pappas et al. (2014). Effort expectancy was assessed with four items, also from Pappas et al. (2014). Social influence was measured using four items sourced from Yang (2010) and San Martín and Herrero (2012). Price-value and hedonic motivation were each measured using three items developed by Venkatesh et al. (2012). Habit was captured using four items also from Venkatesh et al. (2012), and behavioural intention was measured using three items from Chen, Hsu and Lu (2018). More information on the scales is available in Appendix 1. The respondents could indicate their agreement with each statement using a 7-point Likert scale (1 = strongly disagree and 7 = strongly agree).

SmartPLS version 4 was used to test the hypotheses. The guidelines outlined by Hair et al. (2019) were considered for assessing the reflective measurement model and reporting the results of the structural model assessment.

The outer loadings were inspected for indicator reliability. Evidence of indicator reliability is an outer loading of 0.708 and higher. The internal consistency reliability of the multi-item scales was assessed using Cronbach’s alpha and composite reliability (CR). Evidence for internal consistency reliability is an index value of at least 0.7. Convergent validity is established by confirming that each construct’s average variance extracted (AVE) is above 0.5. This study evaluated the multi-item scales used in the survey for discriminant validity using heterotrait-monotrait ratios of correlations (HTMT). The benchmark for discriminant validity in a measurement model is the correlation ratio that does not exceed 0.85.

The structural model was assessed using a three-step process, which involved testing for collinearity, estimating path coefficients and interpreting the model’s predictive accuracy (Hair et al. 2019). R² was used to assess the model’s in-sample predictive accuracy. The PLSpredict algorithm was used to compare the prediction errors of the Partial Least Squares (PLS) path model against simple mean predictions. A positive

value indicates that the prediction error of the Partial Least Squares Structural Equation Modeling (PLS-SEM) results is smaller than the prediction error of simply using mean values (SmartPLS 2024b). The cross-validated predictive ability test (CVPAT) is an alternative to PLSpredict. Cross-validated predictive ability test applies an out-of-sample prediction approach to calculate the model’s prediction error, which determines the average loss value. The average loss value is compared to the average loss value of a prediction using indicator averages (IAs), which is considered a naïve benchmark, and the average loss value of a linear model (LM), which is considered a more conservative benchmark. The difference of the average loss values should be below zero and statistically significant to claim that path model has better predictive capabilities compared to the prediction benchmarks (SmartPLS 2024a). Age and gender were tested separately as moderators, because the moderation hypotheses did not state, for example, that the moderation effect of gender depends on the age of the elderly. As age is a continuous variable in the study, the moderation hypotheses were tested by creating an interaction effect of the independent variable and the moderator. The default data metric of standardisation was used in the moderation analysis to create the interaction effect.

Ethical considerations

Ethical clearance to conduct this study was obtained from the University of Free State and General/Human Research Ethics Committee (No. [UFS-HSD2022/1113/22]).

Results

Assessment of the measurement model

The reliability and convergent validity results of the measurement model are reported in Table 2. All outer loadings exceeded the minimum value of 0.708 and were statistically significant. Therefore, the measurement model met the requirement of indicator reliability. The two internal consistency reliability indices for each construct exceed 0.7. As seen in Table 2, the AVE of each construct was higher than 0.5. Thus, the measurement model demonstrated adequate evidence of reliability and convergent validity following Hair et al. (2019).

The discriminant validity of the measurement model was assessed next. Table 3 presents the HTMT results. For every pair of constructs examined, the HTMT ratio fell below 0.85. This finding confirms the discriminant validity of the measurement model, aligning with the guidelines recommended by Hair et al. (2019).

Testing of the hypotheses

The in-sample accuracy of the structural model was high. The six factors explained 76% of the variance in the respondents’ intention to use mobile grocery shopping services (see Table 4, Model 1). The

value of the path model was 0.747, indicating that the PLS-SEM model offers better predictive performance than simply using mean values. The loss-difference calculated through CVPAT for the IA comparison was −2.665 (p = 0.000) and for the LM comparison was −0.027 (p = 0.612). Thus, it can be concluded that the model has out-of-sample predictive validity based on CVPAT_IA, albeit not strong because of CVPAT_LM not being statistically significant (Capeau, Valette-Florence & Cova 2024).

Performance expectancy did not statistically significantly influence intention to use mobile grocery shopping services (p = 0.384). Therefore, H1 was rejected. Effort expectancy had the strongest positive and statistically significant influence on behavioural intention (β = 0.297; p = 0.000). Subjective influence (β = 0.256, p = 0.000) had the second strongest positive and statistically significant influence on behavioural intention, followed by hedonic motivation (β = 0.213, p = 0.000). Thereafter, price-value (β = 0.158, p = 0.001) exerted a less strong positive and statistically significant influence on behavioural intention, while mobile shopping habit (β = 0.109, p = 0.002) had the weakest positive and statistically significant influence on behavioural intention. Overall, H2, H3, H4, H5 and H6 were accepted.

Model 2 in Table 4 shows that the influence of effort expectancy and hedonic motivation on behavioural intention was moderated by age. The interaction between age and effort expectancy was statistically significant (β = 0.172; p = 0.004). The results of Model 2 also show that the interaction effect between age and hedonic motivation was statistically significant (β = −0.137; p = 0.016). The simple-slope results in Table 5 provide more information about the two statistically significant moderation effects.

Age moderated the influence of effort expectancy on behavioural intention positively. As the age of the respondents increased, the strength of the moderated relationship also increased (see Table 5). Thus, H7b was accepted. The results in Table 5 further reveal that the influence of effort expectancy on behaviour intention is not statistically significant for younger elderly respondents.

Age negatively moderated the positive influence of hedonic motivation on behavioural intention; as the age of respondents increased, the positive influence of hedonic motivation on behavioural intention decreased. H7e was accepted. The simple-slope results for this moderation effect show that the positive influence of hedonic motivation was not statistically significant for older respondents.

Gender only moderated the influence of the habit of using mobile services on behavioural intention. The results in Table 5 show that the positive influence of the habitual use of mobile services on intention was only statistically significant for male respondents, but not for female respondents. Therefore, H8f was accepted.

Discussion

The study set out to identify the factors influencing South African elderly grocery shoppers’ intention to use mobile grocery shopping services. Our study provides evidence of the robustness of the UTAUT2 in explaining behavioural intentions, as the variance in the elderly’s behavioural intention was 76%. Effort expectancy, subjective influence, price-value, hedonic motivation and mobile shopping habit positively influenced the elderly’s intention to use mobile grocery shopping services. Only performance expectancy did not statistically significantly influence behavioural intention.

Grocery shopping is primarily a utilitarian task; therefore, it is reasonable to expect that performance expectancy would influence behavioural intention in the study. This reasoning also aligns with the systematic review by Yap et al. (2022) that included the influence of performance expectancy on the behavioural intention of the elderly technology users. However, in this study, performance expectancy did not influence intention. A possible explanation for this contradictory finding is the smaller screens on mobile phones, coupled with the elderly’s psychological and physical challenges, which could make the elderly doubt the usefulness of mobile grocery shopping applications. It might also be that the elderly have little interest in using mobile grocery shopping applications. Braun and Osman (2024) state that the elderly refuse to engage in new trends and prefer to shop at physical stores.

The strong positive influence of effort expectancy on behavioural intention aligns with Naatu, Selormey and Naatu (2025). The smaller screens of mobile phones, combined with the psychological and physical challenges of the elderly, may have caused the elderly grocery shoppers in the study to doubt the usefulness of grocery shopping apps.

Hedonic motivation had the second strongest influence on behavioural intention. The strong influence may arise from older adults using the entertainment value of mobile shopping to justify their choice in light of the challenges they might perceive in using mobile grocery shopping services (San-Martín, Prodanova & Jiménez 2015).

The positive, statistically significant influence of subjective norms, price-value and habits aligns with prior research using the UTAUT2 to investigate technology adoption in an emerging market. Hoque and Sorwar (2017) reported that subjective influence, a variable similar to subjective norm, had a weak influence on the intention to use mHealth services in Bangladesh. In their study conducted in Iran, Malekpour et al. (2025) reported that perceived price-value and habit positively influence the use of electronic channels for grocery shopping. The focus of the study can explain the weak influence of habit – the elderly’s intention to use mobile grocery shopping services – justifying including (general) mobile shopping habits and not specifically the habit of using mobile grocery shopping services.

The statistically significant moderation effects of age and gender mirror established views regarding age and gender differences in technology adoption. Crucially, the statistically significant moderation effects indicate that such differences can exist in the elderly segment in an emerging market country.

In conclusion, the findings of this study challenge the core assumptions of the UTAUT2 framework by demonstrating that effort expectancy, rather than performance expectancy, is the primary driver of intention among elderly users in emerging markets. This deviation highlights the necessity of testing existing technology adoption frameworks to identify age-related and contextual factors. Additionally, the significant moderation effects of age and gender emphasise that elderly consumers represent a heterogeneous group, reinforcing the importance of tailoring technology adoption models to specific demographic and market contexts.

Theoretical contributions

The study’s contributions to theory building are as follows. Previous research on mobile grocery shopping in emerging markets largely overlooked the elderly, a key demographic for retailers in these countries. In response to the call by Braun and Osman (2024) for more research on the elderly’s use of online shopping channels, this study presents new insights into elderly shoppers’ mobile grocery shopping behaviour in emerging markets.

The UTAUT2 is considered the most comprehensive theory in understanding individual technology adoption and use (Tamilmani, Rana & Dwivedi 2021a). Our study provides more evidence of the robustness of the UTAUT2 in explaining behavioural intentions, as the explained variance in the elderly’s behavioural intention was 76%. The study’s results also support the high predictive validity of the UTAUT2 (Tamilmani et al. 2021a).

Specific theoretical contributions can be presented by comparing the study results to the UTAUT meta-analysis by Blut et al. (2021). The meta-analysis results of Blut et al. (2021) show that performance expectancy should influence behavioural intention more strongly than effort expectancy. In contrast, effort expectancy had a statistically significant influence on intention behaviour in this study and performance expectancy did not influence behavioural intention statistically significantly. The non-statistically significant influence of performance expectancy challenges the key premise that the technology should be useful for it to be considered to be used by an individual (Venkatesh et al. 2003). The influence of hedonic motivation on the behavioural intention of the elderly was stronger than the influence reported by Blut et al. (2021), while the influence of habit was weaker than that reported by Blut et al. (2021). These results present evidence that the behavioural intention of elderly grocery shoppers to use mobile shopping services in an emerging market is influenced by a unique combination of variables. Thus, researchers cannot generalise from previous research which UTAUT variables would influence the behavioural intention of the elderly to use mobile grocery shopping services.

The findings of age and gender differences in the UTUAT2 relationship further elevate the theoretical contributions of the study. In the broader body of knowledge on mobile grocery shopping services, scant evidence of such differences is reported on the elderly’s use of innovative services. The findings on age and gender differences significantly bolster the argument that the elderly market segment in emerging markets is heterogeneous rather than homogeneous.

Managerial implications

As effort expectancy strongly influences behavioural intention among elderly grocery shoppers, South African grocery retailers should focus on minimising the elderly’s perceived effort required for using their mobile grocery shopping app. It is imperative to implement clear and intuitive navigation mechanisms, underscored by descriptive icons, to facilitate ease of use by the elderly. A minimalistic design approach, prioritising essential features, is advocated to streamline user interaction and focus attention on core functionalities. Adopting consistent layout and design patterns is crucial for fostering user familiarity and reducing the cognitive load of learning new interface structures. Accessibility considerations can be improved by ensuring the app supports system-wide font size adjustments. The user interface should also have a high contrast between text and background to improve readability.

Developers and marketers must recognise the importance of hedonic motivation in shaping the user experience for this demographic. South African grocery retailers should integrate features that augment the intrinsic enjoyment of the shopping process, such as personalised recommendations to make the shopping experience more relevant and engaging, and social interaction features can provide a sense of community and belonging.

The price-value ratio can be improved by offering bulk online-only deals to make mobile grocery shopping more attractive for the elderly. Delivery charges can be lowered for elderly shoppers. In addition, South African grocery retailers can designate a day of the week for free delivery for elderly shoppers.

Given the age and gender differences observed, it is vital for grocery retailers in South Africa to recognise in their marketing efforts that the elderly market segment is heterogeneous. Grocery retailers should recognise that older respondents show a stronger positive relationship between effort expectancy and behavioural intention. Tailored displays can be positioned near the entrances of stores and at check-out counters to educate ‘older’ elderly shoppers about the ease of use of mobile grocery shopping services. The individuals shown in these displays should represent different elderly people of different ages. Testimonials of ‘younger’ elderly shoppers can be displayed in-store to market the hedonic aspects of mobile grocery shopping. The habit of using mobile grocery shopping services strongly influenced the intention of elderly male grocery shoppers only. Grocery retailers can partner with other mobile services to market mobile grocery shopping services to elderly male shoppers. The spill-over effect of the positive perceptions of the partner’s mobile service to the grocery retailer’s mobile shopping service would enhance the success of integrated strategies such as advertisements of the grocery retailer displayed within the partner’s mobile service or special promotions attracting elderly male shoppers to trial the mobile grocery shopping service.

Limitations and directions for future research

A limitation of the study is that the research was conducted among the elderly in a single country. In addition, a cross-sectional research design was used. Replication of the study in different emerging market countries is vital to present evidence of the robustness of the findings of this study. Replication will also lead to researchers conducting a meta-analytic structural equation modelling (MASEM) study in the future to confirm the robustness of the results in this study. In such a MASEM, the moderating role of gender and age can be retested on a more significant sample of elderly grocery shoppers. The MASEM study can also explore national culture as a moderator, providing valuable insights for grocery retailers in emerging markets.

The sample represents elderly individuals who live in retirement complexes in two main cities. Therefore, it is vital that researchers in the future replicate the study in low-income groups that have access to this service and elderly consumers living in other types of establishments to determine the robustness of the findings.

The influence of facilitating conditions on the elderly’s intention to use mobile grocery shopping services was not investigated in this study based on the findings by Tan and Ooi (2018). Future research can include facilitating conditions to confirm whether it might be a factor influencing elderly consumers’ intention to use mobile grocery shopping services.

Future research can also extend the UTAUT2 psychological or emotional factors. For example, Huang (2023) recommends that anxiety, utilitarian value and trust are also important predictors of the elderly’s mobile shopping intention. Like grocery retailers in developed markets, grocery retailers in emerging markets are also concerned about retaining elderly grocery shoppers who are already using their mobile grocery shopping service. Thus, future research could examine the continuous use of mobile grocery shopping by the elderly by extending the expectation-confirmation model proposed by Bhattacherjee (2001) with technology readiness variables or by researching cross-channel synergies and dissynergies.

Conclusion

The findings of this research hold significant value for academics and retailers alike. By shedding light on the factors influencing the elderly’s intention to use mobile grocery shopping services in an emerging market country, this study provides valuable insights that can inform future academic research. The study’s results present vital information for academics interested in researching the elderly’s use of mobile services and new technological innovations. Furthermore, the results contribute to filling the gap in the elderly’s use of mobile grocery shopping services. At the same time, the study provides much-needed insights for grocery retailers to enhance the adoption of mobile shops by the elderly, a growing segment in emerging markets and worldwide. For retailers, the managerial recommendations should prompt them to re-evaluate their marketing of mobile grocery shopping services to align with the perceptions of the elderly. In conclusion, grocery retailers should show through their marketing of mobile grocery shopping services that the elderly are just as important a market segment as younger shoppers.

Acknowledgements

The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this research article.

I.S.’s contribution is the study’s conceptualisation, methodology design, data collection, formal data analysis, writing the initial results and project administration. J.N.’s contribution is the concept of the manuscript, formal data analysis, writing the original draft, reviewing and editing the manuscript and supervising the study.

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Data sharing is not applicable to this article as no new data were created or analysed in this study.

The views and opinions expressed in this article are those of the authors and are the product of professional research. They do not necessarily reflect the official policy or position of any affiliated institution, funder, agency or publisher. The authors are responsible for this article’s results, findings and content.

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Appendix 1

Measurement items

PE2: Mobile grocery shopping services would enhance my effectiveness in buying groceries.

PE4: Mobile grocery shopping services would increase my productivity in buying groceries.

EE2: My interaction with mobile grocery shopping services is clear and understandable.

EE4: It would be easy for me to become skilful at using mobile grocery shopping services.

SI1: People who are important to me think that I should use mobile grocery shopping services.

SI2: People who influence my behaviour think that I should use mobile grocery shopping services.

SI3: People whose opinions I value prefer that I use mobile grocery shopping services.

SI4: People around me consider it appropriate to use mobile grocery shopping services.

PV3: At the current delivery cost, mobile grocery shopping services provide good value.

Original Research

Mobile grocery shopping adoption by elderly South African consumers

Ivan Stanley, Jacques Nel

Abstract

Introduction

Prior research on mobile grocery shopping in emerging markets

Model development

Performance expectancy influencing behavioural intention

Effort expectancy influencing behavioural intention

Social influence affecting behavioural intention

Price-value influencing use intention

Hedonic motivation influencing behavioural intention

Mobile shopping habit influencing behavioural intention

Age as a moderator

Gender as a moderator

Methodology

Ethical considerations

Results

Assessment of the measurement model

Testing of the hypotheses

Discussion

Theoretical contributions

Managerial implications

Limitations and directions for future research

Conclusion

Acknowledgements

Competing interests

Authors’ contributions

Funding information

Data availability

Disclaimer

References

Appendix 1

Measurement items