Factors Influencing the Online Grocery Shopping Intention among the Elderly Population in Sri Lanka

Factors Influencing the Online Grocery Shopping Intention among the Elderly Population in Sri Lanka

Kurukulasuriya Weerasinghe Tharindu Madushanka Fernando

Business School, Sichuan University, Chengdu 610065, People’s Republic of China

DOI: https://dx.doi.org/10.47772/IJRISS.2025.90400220

Received: 28 March 2025; Accepted: 05 April 2025; Published: 07 May 2025

ABSTRACT

The adoption of online grocery shopping among elderly consumers remains an understudied area, particularly in South Asian contexts. This study examines the factors influencing elderly individuals’ intention to use online grocery shopping in Sri Lanka. Drawing on the Technology Acceptance Model (TAM) and additional constructs, such as subjective norm, visibility, enjoyment, and situational factors, this research explores the relationships between perceived ease of use, perceived usefulness, and consumers’ intention to use. Data were collected through a web-based survey from 322 elderly respondents, and analysis was conducted using Structural Equation Modeling (SEM). The results confirmed all hypothesized relationships among the research constructs, except for the impact of visibility on perceived usefulness. The findings contribute to the growing body of knowledge on consumer behavior in e-commerce and provide practical insights for marketers seeking to enhance online grocery shopping adoption among elderly consumers. The study also highlights key limitations and calls for future research to explore diverse demographic groups and longitudinal adoption trends.

Keywords: TAM, online grocery shopping, elderly consumers, Sri Lanka, e-commerce

INTRODUCTION

Consumer behavior is inherently dynamic, and consumer preferences change over time. Hence, marketers are required to analyze consumers’ purchasing patterns, preferences, and the extent to which products meet consumer expectations. This ongoing process presents numerous research opportunities within the marketing discipline. Moreover, the rapid evolution of digital platforms has further accelerated changes in consumer behavior, making it essential for marketers to adopt agile and adaptive strategies. The growth of e-commerce has been a major topic of discussion in marketing domain for a long time. Recognizing the need to enhance their marketing strategies, many companies have increasingly leveraged digital platforms to expand their customer reach and strengthen market engagement. In response, marketers are increasingly adopting digital marketing and communication strategies, as these enable the delivery of real-time, personalized services and content tailored to individual consumers (Holliman & Rowley, 2014).

According to Solomon (2010), consumers have various attitudes toward shopping, as they possess different thoughts, emotions, and preferences rather than a uniform perspective. Consequently, consumer online shopping behavior has long been a central research focus in the field of e-commerce (L. Chen, 2009). Liang & Lai (2000) described online shopping behavior as the process of purchasing products and services via the Internet, consisting of five steps similar to those in traditional shopping behavior. Furthermore, the COVID-19 pandemic changed consumer behavior worldwide, particularly in the shopping experience (H. Chen et al., 2021). Accordingly, Goddard (2020)concluded that there was a significant shift in people’s lifestyles, ultimately leading to a substantial increase in the number of consumers using online platforms for shopping. Given these developments, researchers and marketers continue to explore strategies that can optimize digital shopping experiences.

Upon reviewing previous literature on online buying behavior, it is possible to identify numerous factors influencing consumer decisions. Researchers such as Li et al., (1999) highlight demographics, shopping orientations, perceived channel utility, and channel knowledge as key determinants. More studies have discovered that, compared to offline shoppers, online shoppers are likely to be older, better educated, have higher incomes, and are more familiar with technology (Bellman et al., 1999; Donthu & Garcia, 1999; Swinyard & Smith, 2003). Another study examining gender differences in web-based shopping found that men are more likely to purchase products or services online than women (Garbarino & Strahilevitz, 2004; Van Slyke et al., 2002). In their study, Karayanni (2003) identified time efficiency, avoidance of crowds, and 24-hour shopping availability as key reasons for shopping online.

Researchers such as Levin et al., (2003) and A. Chen et al., (2015) argue that online purchases are more likely to be made for products characterized by higher levels of intangibility. Additionally, studies examining the types of products and services in the context of online shopping have identified behavioral differences between consumers seeking information through online and offline channels. Accordingly, Ye et al., (2011) confirm that consumer behavior varies depending on the channel used, and Wu et al., (2011) discuss the importance of establishing new channels to improve customer relationships. Furthermore, researchers have analyzed the differences between online and offline shopping by examining sociocultural attributes and their effects on consumer behavior (Hwang & Jeong, 2016). Most research has used differences in demographic and socioeconomic factors as a basis for such studies.

Many countries in Asia are taking advantage of e-commerce by opening up (Waghmore, 2012), and Sri Lanka is no exception to this global phenomenon. In fact, according to Kemp (2025), internet usage in Sri Lanka has grown remarkably, rising from 30% in 2018 to an impressive 53.6% by early 2025. They also reported a 6.4% increase in internet adoption between January 2024 and January 2025. This rapid growth in internet penetration has been driven by increased smartphone adoption, improved digital infrastructure, and more affordable data plans. As a result, Sri Lanka’s business landscape has undergone a transformation, with the e-commerce sector experiencing significant expansion. The rise of online payment solutions and fintech innovations has further facilitated seamless digital transactions, making online shopping more accessible and convenient for consumers. E-commerce transactions in Sri Lanka are expected to grow significantly in the near future, and Sri Lankan online shoppers are increasingly searching for and purchasing retail products for their everyday needs (Athapaththu & Kulathunga, 2018).

Online shopping requires consumers to engage with technology to purchase products and services. Information systems replace the physical shopping environment with a digital experience, enabling consumers to browse, evaluate, and purchase products in an electronic setting. Nevertheless, online shopping behavior is a complex socio-technical phenomenon. Given rapid technological advancements and the evolving nature of consumer behavior, examining the factors that influence online shopping behavior remains a critical area of research within e-commerce and the broader marketing discipline. The present study investigates the factors influencing online grocery shopping among elderly individuals in Sri Lanka, addressing a significant research gap in the e-commerce landscape. While e-commerce adoption and customer satisfaction have been widely studied, there is a notable lack of research on the underlying factors driving online purchasing intentions among older consumers, particularly in the Sri Lankan context.

Findings from previous studies indicate that online shopping acceptance and behaviour differ across nations and cultures (C. Park & Jun, 2003). Online grocery shopping has been examined from various perspectives, resulting in a comprehensive body of literature on diverse acceptance models. However, most studies primarily focus on the adoption of online grocery shopping in the United States and European markets. To develop a broader understanding of this research area, further studies should be conducted in culturally and economically diverse countries beyond those examined in existing literature. Hence, this study contributes to knowledge by exploring an underexamined demographic in a developing country within the South Asian region. The remainder of the paper is organized into the following sections: the literature review and hypotheses, research methodology, statistical analysis and discussion, and conclusion.

LITERATURE REVIEW AND CONCEPTUAL MODEL

Online Grocery Shopping

Online grocery shopping is a subset of e-commerce that enables consumers and businesses to purchase food items and various household supplies, including perishable goods. Some researchers have identified two key reasons why consumers switch to online grocery shopping. These studies highlight that consumers often perceive time savings and the ability to purchase specialized products unavailable in nearby physical stores as valuable benefits of online grocery shopping (Berg & Henriksson, 2020; Blitstein et al., 2020; Piroth et al., 2020). Consequently, market segments for online grocery shopping can primarily be categorized based on convenience, service and price (Frank & Peschel, 2020). One of the earliest studies on this subject, conducted by K. Park et al., (1996), found that house shopping services were adopted to enhance convenience in consumers’ daily lives. However, the study also highlighted the consumers’ concerns regarding security risks and other perceived risks. Similarly, Hiser et al., (1999) examined consumers’ willingness to adopt online grocery shopping in Texas, USA. Their findings indicated that convenience and perceived risk were key influencing factors, with older consumers being less likely to engage in online grocery shopping. Morganosky & Cude (2000) examined consumer responses to online grocery shopping in the United States and identified convenience and time-saving as the primary motivators for adoption.

Similarly, Childers et al., (2001) explored both hedonic and utilitarian motivations for online grocery shopping using the Technology Acceptance Model (TAM) and found that Perceived Usefulness (PU) and Perceived Ease Of Use (PEOU) were strong predictors of online grocery shopping adoption. Additionally, Raijas (2002) investigated the benefits and challenges associated with online grocery shopping in Finland, confirming that convenience and PEOU were key factors influencing consumer acceptance. In Sweden, Hansen (2008) conducted a study on online grocery shopping and consumer values, applying the Theory of Planned Behavior (TPB) Ajzen (1991)) and confirming its relevance in this context. The study further revealed that self-transcendence and openness to change have no significant impact on consumers’ attitudes toward online grocery shopping. In the United Kingdom, Hand et al.,(2009) examined the impact of situational factors on online grocery shopping and found that such factors play a crucial role in determining whether consumers continue or discontinue their use of online grocery shopping. Similarly, Hui & Wan (2009) conducted a study in Singapore and identified PEOU and PU as strong predictors of behavioral intentions toward online grocery shopping. Their findings also highlighted the influence of demographic factors, such as age, education, and income, on consumers’ intention to adopt online grocery shopping. Cultural and geographic factors may also play a significant role in shaping online grocery shopping behavior. Delfmann et al. (2011) conducted a study across four European countries and discovered that consumers’ motivations for online grocery shopping were different based on their country of residence.

However, above specific motivations for using online grocery shopping may not apply to all consumers, as individual characteristics and situational factors can influence their decision to purchase groceries online (Dominici et al., 2021). For instance, the COVID-19 pandemic abruptly forced consumers to modify their shopping behaviors, accelerating the shift toward online channels as the preferred method for purchasing groceries and other essential goods (Sheth, 2020). In recent years, grocery retailers have responded to the increasing demand for online shopping by developing e-commerce platforms, including websites and mobile apps. Despite these technological advancements and the growing accessibility of online grocery shopping, the grocery sector continues to exhibit lower e-commerce adoption rates compared to other consumer goods. Previous research has utilized various theories to explain online grocery shopping behavior, integrating both technological and psychological perspectives. In particular, the TAM is a prominent framework used to explain online grocery purchase intentions. The constructs within this model vary considerably, and researchers have often introduced new elements to better understand the mechanisms that influence online grocery shopping behavior. This study will employ an adapted version of the TAM for the proposed investigation.

Technology Acceptance Model

The Technology Acceptance Model (TAM) is the most widely used framework for explaining an individual’s acceptance of a particular information system (Lee et al., 2003). It was first developed by Davis (1989) based on the Theory of Reasoned Action (TRA)(Ajzen & Fishbein, 2000) to explain user responses to technology in workplace environments. Since its development, the model has been widely utilized in various studies to analyze and explain the factors influencing users’ acceptance or rejection of information technology (Legris et al., 2003). Davis (1989) identified two key constructs: perceived ease of use (PEOU) and perceived usefulness (PU), as primary factors influencing on individuals’ attitudes toward a particular technology. He defined PU as “the degree of which a person believes that using a particular system would enhance his or her job performance” and PEOU as “the degree to which a person believes that using a particular system would be free from effort.” He further proposed that external variables should be identified to better explain PU and PEOU.

Although TAM is a highly adoptable model, various researchers have updated the model with numerous constructs to strengthen its explanatory power (Legris et al., 2003). Venkatesh & Davis (2000) eliminated the ‘attitude’ variable and incorporated additional variables including subjective norm, experience and output quality in their extended version of the model. Scholars such as Lederer et al. (2000), Teo et al. (1999), J.-H. Wu & Wang (2005) among others supported and validated this decision in their studies. TAM has been examined and confirmed across diverse contexts involving various technologies, scenarios, control factors, and user groups (Lee et al., 2003). As a result, it is recognized as a robust and reliable model.

Several scholars have highlighted the popularity of TAM in assessing the acceptance of technology within a given population, as it is specifically designed to evaluate adoption in the context of information technology. These studies further demonstrate that TAM is supported by a well-established theoretical framework and has garnered strong empirical support from previous research (King & He, 2006; Yousafzai et al., 2007). Many researchers have tested the applicability of the TAM in relation to internet and e-commerce usage. When applied to the online context, the online consumer is viewed as a computer user, and the web store is considered a technological system (Kuofaris, 2002). Additionally, many studies have tested and validated the two major variables of TAM model: PU and PEOU. Their importance and appropriateness as constructs in the context of e-commerce has been recognized (Lederer et al., 2000; Teo et al., 1999). Therefore, PU and PEOU should be considered when evaluating the acceptance and usage behaviors related to e-commerce activities.

In addition to PU and PEOU, two other key variables in the TAM are intention to use (ITU) and actual usage. In the context of the present study, ITU will represent the intention of Sri Lankan consumers to engage in online grocery shopping. However, this study excludes ‘actual usage’ as a construct from the model, as it focuses solely on self-reported habits rather than direct measurement of usage behavior. Actual usage requires a detailed and continuous tracking of participants’ behavior, which may not be feasible within the scope of this study. By focusing on intention rather than actual usage, the study can better capture the psychological and social factors that drive potential adoption. Furthermore, this adapted version of the TAM will integrate several external variables to provide a more nuanced understanding of consumer motivations.

Many studies have supported the relationship that “the belief in a system’s perceived ease of use (PEOU) can serve as a predictor for its perceived usefulness (PU)” (King & He, 2006). In other words, PEOU positively influences PU at a significant level. This relationship has been confirmed by numerous studies across various fields, including e-commerce, mobile commerce and internet banking (Ha & Stoel, 2009; Lai & Li, 2005; J.-H. Wu & Wang, 2005). In their study, Saadé & Bahli (2005) found a positive relationship between PEOU and ITU. This relationship was further supported by another study related to internet banking in Malaysia (Lallmahamood, 2007). Furthermore, a research on the intention to use the internet among senior citizens in China by Pan & Jordan-Marsh (2010) confirmed this positive relationship as well. A research that tested the consumers’ acceptance of e-commerce demonstrated a positive relationship between PU and ITU (Klopping & McKinney, 2004). This relationship has been further confirmed in various technological fields, including mobile wireless technology and cloud computing for education (Bhatiasevi & Naglis, 2016; Kim & Garrison, 2009). Grounded in above findings, the following hypotheses are proposed:

H1: There is a positive significant relationship between PEOU and PU of online grocery shopping.

H2: There is a positive significant relationship between PEOU and ITU of online grocery shopping.

H3: There is a positive significant relationship between PU and ITU of online grocery shopping.

Integrating External Variables to TAM

To enhance the explanatory power of the TAM in the context of online grocery shopping, this study incorporates several external variables. Ajzen & Fishbein (2000), in his Theory of Reasoned Action (TRA), defined Subjective Norm (SN) as a “person’s perception that most people who are important to him think he should or should not perform the behavior in question.” Important figures in consumers’ lives, such as family, friends, authority figures, or media, exert influence that shapes their perceptions of subjective norms. In the context of the present study, consumers are more likely to engage in online grocery shopping if they perceive that influential individuals in their surroundings approve of it. Venkatesh & Davis (2000), in their extended TAM, explains that SN has a positive relationship with PU. Schepers & Wetzels (2007) studied the overall influence of SN within the TAM framework and concluded that there is a positive relationship between SN and PU. Another study conducted by Kim & Garrison (2009) on consumers’ willingness to adopt mobile technology for purchasing fashion goods in the United States also corroborated these findings. Therefore, this study hypothesize that:

H4: There is a positive significant relationship between SN and PU of online grocery shopping.

Visibility (VIS) is another factor which has been associated with TAM. According to the Diffusion of Innovation Theory, visibility is defined by Rogers (2010) as the degree to which an innovation is noticeable to potential adopters. Investigating the impact of VIS on attitudes toward information technology acceptance, Karahanna et al., (1999) discovered a significant positive relationship. In their study on online grocery shopping acceptance in Australia, Chien et al., (2003) identified VIS as a key factor influencing the community acceptance. However, these studies utilized the original TAM, which included the variable ‘attitude.’ This variable was later removed in the extended version of TAM introduced by Venkatesh & Davis (2000). Furthermore, the relationship between VIS and PU received partial support from Miller & Khera (2010)’s study on digital library acceptance. Their cross-cultural study further revealed that role of VIS changes throughout the cultures. Therefore, this study hypothesize that:

H5: There is a positive significant relationship between VIS and PU of online grocery shopping.

Enjoyment (ENJ) is another variable that, in the context of the proposed study, can be defined as the degree to which consumers find the process of online grocery shopping to be enjoyable. Venkatesh (2000) explored the impact of PEOU on user acceptance by incorporating ENJ into his research model. His findings revealed that as users gained more experience, their ENJ had a positive influence on PEOU. Mun & Hwang (2003) conducted a study on the prediction of web-based information systems usage. While their study confirmed a positive relationship between ENJ and PEOU, it also revealed a similar positive relationship between ENJ and PU. Similarly, T. Teo & Noyes (2011) also confirmed both relationships in their study. Furthermore, studying the college students’ e-commerce acceptance, Ha & Stoel (2009) confirmed the relationship between ENJ and PU. Therefore, this study hypothesize that:

H6: There is a positive significant relationship between ENJ and PU of online grocery shopping.

H7: There is a positive significant relationship between ENJ and PEOU of online grocery shopping.

Furthermore, situational factors (SF) can serve as key triggers influencing consumers to buy groceries online. A study by Hand et al., (2009) revealed that many respondents attributed their adoption of online grocery shopping to lifestyle changes (e.g., relocation, having small children at home). However, it was also found many consumers discontinued online grocery shopping once the initial trigger disappeared or if they encountered service-related issues. Dabholkar (1996) identified lack of mobility as a SF influencing online shopping adoption. Another study by Kvalsvik (2022) found that mobility limitations, health concerns, and distance to physical stores are key SF that drive older adults to opt for online grocery shopping. In recent years, the COVID-19 outbreak has been a significant SF influencing many consumers to adopt online grocery shopping, driven by safety concerns, movement restrictions, and the need for contactless transactions (Baarsma & Groenewegen, 2021; Dannenberg et al., 2020). Based on above, it is evident that SF can positively influence the consumer’s intention to buy groceries online. Therefore, this study hypothesize that:

H8: There is a positive significant relationship between SF and ITU of online grocery shopping.

As illustrated in Figure 1, this study propose 8 hypotheses to test the relationships between identified variables.

Figure 01: Conceptual model

METHODOLOGY

The present study adheres to the positivist philosophy and employs a quantitative research design. Since the primary objective is to identify and validate the hypothesized relationships within the proposed conceptual model, a web-based questionnaire was chosen as the data collection method. Given the increasing internet penetration in Sri Lanka, this approach aligns well with the target population, ensuring both efficiency and cost-effectiveness in data collection.

Instrument Development

The questionnaire consists of two sections. ‘Section A’ presented questions about the respondents’ demographic profile. The conceptual model consists of seven constructs, each measured using pretested questionnaire items sourced from existing literature. As a result, a total of 31 items were included in the ‘Section B’ of questionnaire (refer Table 1). These items were presented as statements, and respondents were asked to express their level of agreement using a five-point Likert scale, ranging from (1) ‘Strongly Disagree’ to (5) ‘Strongly Agree’. After conducting a pilot test, necessary adjustments were made to the questionnaire to ensure that the survey items were clear, unambiguous, and easy for respondents to understand.

Data Collection

The target sample for this study comprised elderly individuals who are familiar with technology and have access to the internet. Since identifying a sufficiently large sample of such respondents through simple random sampling posed challenges, the researcher employed the snowball sampling technique to reach more participants. This non-probability sampling method was particularly useful given the study’s constraints related to time, cost, and accessibility. The survey link to the developed questionnaire was made accessible to the elderly population in two major cities in Sri Lanka over a 90-day period, from November 2024 to February 2025. By the end of the data collection period, a total of 473 questionnaires had been completed and returned. However, 151 responses were excluded due to invalid or incomplete data. Consequently, 322 questionnaires were retained for the final sample.

Table 1 – Summary of questionnaire items

RESULTS AND DISCUSSION

Descriptive Statistics

A total of 322 participants provided valid responses to the survey. The demographic characteristics of the respondents are summarized in Table 2. Analyzing the data, it is evident that the number of female participants is nearly twice that of male participants. This disparity may be attributed to Sri Lanka’s cultural context, where women traditionally manage household responsibilities, including grocery shopping, while men are less involved in such activities. Furthermore, the majority of respondents fall within the 55–64 age group, with a decline in the number of online grocery shoppers as age increases. This trend could be explained by the challenges older individuals face in adapting to new technology or their lack of experience in using online shopping platforms. The findings also indicate that most online grocery shoppers have completed at least a middle school education, with a significant proportion holding a bachelor’s degree or higher. This suggests a correlation between education level and the adoption of online grocery shopping. Additionally, the majority of respondents belong to the medium-income category, earning between 40,000 and 80,000 LKR per month. In terms of experience with online grocery shopping, most participants have been using such platforms for one to three years, while only a small percentage have been using them for more than five years.

Table 2 – Demographic characteristics of the sample

Preliminary Analysis and Measurement Validity

This study employs Structural Equation Modeling (SEM), which is widely recognized for its effectiveness in analyzing complex relationships. According to Hair et al., (2013), a general guideline suggests a minimum sample size of at least 10 cases per item for SEM. Therefore, this study requires a sample of 31 × 10 = 310, confirming that the current sample is sufficient. Consequently, data analysis was conducted using SPSS and AMOS software. Using SPSS, the Kaiser-Meyer-Olkin test (KMO = 0.81) and Bartlett’s Test of Sphericity (p < 0.05) were conducted, yielding satisfactory results. Therefore, factor loading analysis was performed to remove items with low loadings or cross-loadings. However, all 31 initial items were retained, representing the seven constructs of the conceptual model. Additionally, all Variance Inflation Factor (VIF) values were below 5 for each item, indicating no significant multicollinearity within the data (Montgomery et al., 2021). Reliability and validity tests indicated that Cronbach’s alpha values ranged from 0.767 to 0.913, while composite reliability (CR) values varied between 0.771 and 0.926. Both were within the acceptable level of 0.7 (Hair et al., 2013). These results are shown in the Table 3.

Table 3 – Assessment of data suitability and validity

Structural Model and Hypothesis Testing

By examining the correlation matrix (Table 4), the discriminant validity of the measurement items can be assessed. Discriminant validity is supported as the pairwise correlation estimates between any two constructs were below 1. Furthermore, convergent validity is established since the Average Variance Extracted (AVE) values ranged from 0.527 to 0.774, all exceeding the recommended threshold of 0.50 (Fornell & Larcker, 1981).

Table 4 – Correlations matrix

**Correlation is significant at the 0.01 level (2-tailed).

The model’s goodness-of-fit (GOF) was evaluated using multiple fit indices to assess how well the proposed model aligns with the observed data. It was revealed that the model met the recommended criteria across multiple indices. The values for GFI (0.90), CFI (0.94), TLI (0.93), and NFI (0.91) all surpassed the commonly accepted threshold of 0.90, suggesting a strong model fit. Furthermore, both RMSEA = 0.062 and SRMR = 0.055 fell within the acceptable range as shown in the Table 5.

Table 5 – Model fit indices

The structural model was analyzed using SEM to evaluate the proposed relationships among variables. Using AMOS software, the parameters of the structural model were estimated. Based on the estimated path coefficients, all hypothesized relationships were tested simultaneously. Table 6 presents the results of hypothesis testing, and as indicated, one hypothesis failed to achieve statistical significance.

Table 6 – Summary of hypotheses testing

***p < 0.001; **p < 0.01; *p < 0.05

Firstly, the positive relationship between PEOU and PU (H1: β = 0.49, t = 7.74, p < .001) was supported. This finding suggests that consumers are more likely to perceive online grocery shopping as useful when they find it easy to use. Furthermore, ITU is positively influenced by PEOU (H2: β = 0.74, t = 10.63, p < .001), indicating that consumers who perceive online grocery shopping as easy to use are more likely intend to use it. Likewise, PU significantly influences ITU (H3: β = 0.72, t = 9.17, p < .001), suggesting that consumers who find online grocery shopping useful are more inclined to continue using it. SN have a positive impact on PU (H4: β = 0.48, t = 6.72, p < .001). This finding suggests that the social environment and the influence of people around consumers play a significant role in shaping their perception of the usefulness of online grocery shopping.

The relationship between ENJ and PU was statistically supported (H6: β = 0.61, t = 5.16, p < .001), indicating that consumers who perceive online grocery shopping as enjoyable are more likely to consider it useful. Similarly, ENJ was found to have a positive relationship with PEOU (H7: β = 0.34, t = 4.43, p < .001). This finding suggests that when consumers perceive online grocery shopping as enjoyable, they are more likely to view it as easy to use. The positive relationship between SF and ITU (H8: β = 0.64, t = 8.01, p < .001) was also statistically supported, indicating that situational factors can significantly influence consumers’ intention to adopt online grocery shopping.

However, the relationship between VIS and PU (H8: β = 0.08, t = -1.94, p = 0.72) was not statistically supported. This finding suggests that, although consumers may observe others using online grocery shopping, this visibility does not necessarily lead them to perceive it as useful. This result could be attributed to the nature of the sample studied. A potential explanation is that elderly consumers may have limited experience with online grocery shopping, preventing them from fully appreciating its usefulness, even if they see others using it. Their comfort with traditional shopping methods, along with potential barriers such as limited access to devices or difficulty in using them, may further hinder their ability to recognize the usefulness of online grocery shopping.

CONCLUSION

Theoretical and Practical Contributions

This study contributes to the growing body of knowledge on consumer behavior in online grocery shopping by examining the factors influencing elderly consumers’ adoption of such platforms in a developing country context. It fills a research gap by exploring online grocery shopping adoption in Sri Lanka, a largely underrepresented South Asian market. The findings are particularly significant as they offer a nuanced understanding of an age group that has traditionally been slower to embrace e-commerce. From a theoretical perspective, this study extends the applicability of TAM by demonstrating that factors beyond PEOU and PU, such as enjoyment, social influence, and situational factors, play a crucial role in shaping online shopping behavior among elderly consumers. The finding that VIS does not significantly impact PU challenges conventional assumptions and opens new discussions. Traditionally, VIS has been considered a key determinant of PU, particularly in online environments. However, the lack of a significant relationship in this study suggests that elderly consumers may rely less on visual stimuli and more on other factors. This indicates the need to reconsider the role of VIS in technology acceptance frameworks, especially for age-specific user groups. In terms of practical implications, marketers and online grocery retailers should focus on enhancing ease of use and enjoyment to increase adoption rates among elderly consumers. User-friendly interfaces, simplified navigation, and personalized recommendations can help mitigate technological barriers. Similarly, providing dedicated customer support services such as live chat, phone assistance, or video tutorials can also build confidence and reduce anxiety among elderly users. Additionally, marketers can leverage social influence by encouraging family members and peers to advocate for online grocery shopping.

Limitations and Future Research Directions

Despite its contributions, this study has several limitations. First, the reliance on a non-probability sampling method may have excluded less tech-savvy elderly individuals, introducing selection bias and limiting the generalizability of the findings. Second, the study is limited to elderly consumers in Sri Lanka. This restricts the applicability of the results to other regional or global contexts, where cultural, economic, and infrastructural differences may shape online shopping behaviors differently. Additionally, self-reported data may have led to response biases, with participants potentially over- or underestimating their attitudes and behaviors. Future research could conduct comparative studies across diverse geographic and cultural settings to explore the extent to which these findings are globally consistent or context-specific. Additionally, this study encourages a more inclusive research approach by recommending mixed methods, including qualitative interviews, offline data collection, and longitudinal research to track behavioral changes over time.

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