The crucial role of e-logistic service quality to integrated theories to predict continuance intention on fresh produce e-commerce

Abstract

This research aims to comprehensively understand the determinants influencing customers’ adoption of fresh produce e-commerce services by integrating the expectation-confirmation theory of the technology acceptance model (EC-TAM), the product evaluation model, and e-logistics service quality (E-LSQ). In serving fresh perishable products, the quality of logistics services plays a vital role in bridging the quality of the products presented on the application page and the actual product quality experienced by customers. Emphasizing the mediating role of E-LSQ, the study underscores its direct and indirect effect on fostering continuance usage intention. Employing a robust quantitative approach, data from 364 Jakarta, Indonesia participants were systematically gathered through online questionnaires. The analysis, conducted through the structural equation modeling in AMOS 24, highlights the pivotal role of user confirmation in shaping E-LSQ. Consequently, E-LSQ emerges as a substantial determinant, exerting a pronounced impact on the perceived product quality, heightened customer satisfaction, and the cultivation of intentions to use fresh produce e-commerce services in the future. The originality of this research lies in the innovative integration of theories, providing a nuanced exploration of the interplay in the context of fresh produce e-commerce. The contribution extends beyond theoretical advancement to practical implications, offering valuable insights for businesses aiming to enhance customer loyalty in the burgeoning e-commerce sector, specifically within fresh produce.

Keywords:

• Fresh produce e-commerce
• confirmation of electronic performance
• product quality
• e-logistic service quality
• continuance intention

REVIEWING EDITOR:

• Dr Kaouther Kooli, Bournemouth University, United Kingdom of Great Britain and Northern Ireland

SUBJECTS:

• Economics
• Business, Management and Accounting
• Marketing

Introduction

Online shopping services are an alternative that consumers use apart from traditional shopping methods, such as shopping in physical stores. Online shopping services are often additional (complementary) shopping services provided by physical stores. Some customers choose traditional shopping services through physical stores because they want to see the product directly, feel the shopping atmosphere, get a shopping experience, and interact socially with the seller directly. In general, physical and online shopping services can coexist and complement each other according to customer preferences and needs. Simmons et al. (2022) stated that traditional grocery retailers must determine their value proposition to strengthen their competitiveness by considering partnerships to develop online shopping services. As Ken Research’s findings state, currently, there are 4.4 million traditional grocery retailers in Indonesia. However, the average frequency of online shopping is 3 to 5 times a month, indicating that consumers are interested in online shopping (Agarwal & Jain, 2022). The increase in internet users encourages increased use of online shopping and payment services. So, (Nurhayati-Wolff, 2023) stated that almost all internet users in Indonesia shop via e-commerce to meet their monthly needs.

The results of the Statista survey state that five countries dominate the online grocery shopping market share in the Asia-Pacific Region, namely China, Indonesia, India, South Korea, and Australia (Ganbold, 2023). Among these five countries, China leads in online grocery shopping in terms of market size and market penetration. E-grocery customers in China cover all age groups, from young people to seniors. Meanwhile, based on the product type, most customers in China and Australia generally shop online to buy fruit and vegetables. This result differs from Indonesia and South Korea, which use e-grocery services to shop for fresh and frozen food products.

Several alternative online grocery shopping models in Indonesia include omnichannel, aggregator business models, marketplaces, and fresh produce e-commerce models. Omnichannel services are integrated services grocery business owners provide that combine offline and online services (Sethi & Bhutada, 2021). Omnichannel service is a strategy to improve the shopping experience through various communication media channels (Gu et al., 2019; Hole et al., 2019; Hübner et al., 2016). Lottemart, Alfagift, and Klik Indomaret present several e-grocery applications based on omnichannel services. Besides omnichannel services, online grocery sales are also through an aggregator business model. The aggregator business model is an e-grocery application that connects consumers with online grocery shopping services from various supermarkets and traditional markets (Verma, 2020). This application supports online ordering and payment services—Several e-grocery aggregator business models exist, including Biblimart, Happyfresh, and Titipku. Meanwhile, the marketplace is a platform that gathers sellers and brings them together with buyers (Sorescu et al., 2011; Zambon et al., 2019). The marketplaces that provide fresh grocery purchasing services include Shopee Supermarket. Furthermore, the fresh produce e-commerce platform is one e-grocery service model format that sells fresh products such as vegetables, fruit, dairy milk, and fresh meat online (K. Zhao et al., 2021). The fresh produce e-commerce in Indonesia includes Sayurbox, Carisayur, and Segari.

The rapid increase in the use of online shopping services in Indonesia began with situational factors that occurred during the COVID-19 pandemic (Handayani et al., 2020; Hartono et al., 2021). However, in the post-COVID-19 pandemic era, when all life returns to normal, it is a big challenge how e-grocery services can survive to remain in customer demand. Monoarfa et al. (2023) stated that consumers tend to continue using e-grocery services when e-grocery services provide quality services that can add value to shopping. Furthermore, Jain et al. (2021) explain that supply availability, delivery timeliness, and product condition are the main factors that support customer satisfaction and intention to shop again through e-grocery shopping services. Then, Guan et al. (2022) stated logistics operations, delivery speed, and product quality. Product freshness and packaging quality are the most important things to maintain the quality of fresh food ingredients (Kaswengi & Lambey-Checchin, 2019). Regarding the organization and timeliness of delivery, survey results obtained by McKinsey & Company stated that the average delivery time expected by customers ranges from 3 to 120 minutes menit (Simmons et al., 2022).

The text identifies several research gaps in fresh produce e-commerce, including understanding customer characteristics, factors influencing repeat purchases, and the impact of logistics service quality. It also notes the need to explore post-pandemic customer preference shifts and develop an integrated theoretical framework. Empirical validation and practical strategies for service providers are also needed. Addressing these gaps will improve the understanding and sustainability of fresh produce e-commerce.

Hence, investigating the determinants influencing customers’ sustainable choice necessitates further research. Post-pandemic shifts in customer preferences pose challenges for fresh produce e-commerce, demanding ongoing attractiveness. The decision to continue using fresh produce e-commerce services require a review of integrated theories, encompassing the user’s acceptance of information system, consumers’ product evaluation, and electronic logistic service quality. Thus, several research questions underlying this research include: 1) What are the characteristics of potential customers for fresh produce application services; 2) What factors encourage repeat purchase intentions through fresh produce application services, and 3) How important is the quality of logistics services to support repurchase intentions through fresh produce application services?

This research will explain it using a theoretical approach through a literature review, building a conceptual framework, and developing a hypothesis to explain the relationship between variables that influence continued intention to use fresh produce e-commerce. To test the theoretical implementation in the real world, this research surveyed to evaluate customer behavior when shopping through fresh produce e-commerce services. Next, the customer survey data will be processed and analyzed to answer research questions. The findings of this research will contribute scientifically to providing a new perspective on the development of marketing theories, especially in the online shopping service industry for fresh food products. In addition, the findings of this research will contribute practically to fresh produce e-commerce service providers in designing strategies to increase repurchase intentions.

Literature review

Expectation-confirmation theory (ECT)

Darley et al. (2010) modified the Engel-Kollat-Blackwell purchasing decision model, incorporating additional external factors to elucidate influences on online purchases, such as individual characteristics, social influences, situational and economic factors, and the online environment. The Expectation-Confirmation Theory (ECT) is a widely employed framework in consumer behavior literature, frequently utilized to examine consumer satisfaction and post-purchase behaviors like repurchasing (Oliver, 1999). It is frequently used to examine consumer satisfaction and post-purchase behaviors such as repurchasing. ECT posits that individual expectations shape beliefs, attitudes, and behaviors toward a product or service, consisting of four primary construct: expectation, perceived performance, disconfirmation of beliefs, and satisfaction (Lin et al., 2009). The expectation-confirmation paradigm asserts that consumers satisfaction hinges on their initial expectation and discrepancies between expected and actual service performance (Fu et al., 2018; Thong et al., 2006).

In online marketing of fresh produce, the product information displayed on the application page often needs to match the actual quality of the product as perceived by the customer upon receipt. The delivery time lag significantly contributes to the discrepancy between product information and actual product quality (Ali et al., 2022). Moreover, fresh produce tends to be perishable and has a short shelf life (Kaswengi & Lambey-Checchin, 2019). Zhao and Bacao (2020) in their study stated that applying the Expectation-Confirmation Theory (ECT) as the foundation for the Expectation-Confirmation Model (ECM) is effective in comparing customer expectations and actual performance to determine satisfaction and continuance intention to order food catering, integrating it with the Unified Theory of Acceptance and Use of Technology (UTAUT) to consider the contribution technology performance and social influence. In other contexts, Jumaan et al. (2020) dan Park (2020) analyzed the influence of elements within ECT and the Technology Acceptance Model (TAM) to predict the intention to use digital devices. In response to previous research, this study applies ECT as a foundation to integrate several other theories considered as factors driving continuance intention to use fresh produce applications—offering a novel research approach. The researcher considers the importance of technology performance as a support for e-commerce services, in addition to perceived product quality, which reflects product freshness, and e-logistic service quality, which plays a role in the product information service and delivery.

ECT in technology acceptance model (EC-TAM)

Davis (1989) put forward the technology acceptance model (TAM) to explain that the evaluation factors of electronic service users regarding ease of use and benefits drive attitudes and intentions to use electronic services. Perceived ease of use is a form of self-efficacy, which is a factor that influences perceived usefulness. In short, the higher a person’s confidence in using electronic services, the higher the benefits they will feel from using them (Shukla & Sharma, 2018; Wang & Zhang, 2020).

Furthermore, Bhattacherjee (2001) and (Zhao and Bacao (2020) integrated TAM into expectation-confirmation theory (ECT), predicting how perceived ease of use and usefulness generate satisfaction-dissatisfaction, and the intention to use electronic services. For experienced users, perceived ease of use is not an issue, so the question is how to confirm the user’s agreement between expectations and the actual performance of electronic services. In other words, EC-TAM proposes two main performance drivers: confirmation and perceived usefulness. These factors jointly influence customer satisfaction and, consequently, foster the desire to persist in using the information system (Wu et al., 2020). Brown et al. (2012) reinforced the finding of Bhattacherjee (2001), emphasizing the importance of expectations and experiences in influencing actual usage in information systems. Moreover, Jumaan et al. (2020) extended the expectation-confirmation model to predict the continued usage of mobile internet services. The study identified perceived usefulness and satisfaction as pivotal determinants of continuance intention.

ECT in buyer’s product evaluation

Dodds et al. (1991) explained that the consumer process of evaluating products based on perceived value is an essential factor influencing willingness to buy. Perceived value is the result of consumer assessment by comparing the perceived value received by consumers and the perceived sacrifice paid by consumers to obtain products and services. The study found that a buyer’s product evaluation is based on several factors, such as product price and brand/store reputation, reflecting product quality and becoming a driving force for purchasing decisions. Another study stated that in addition to factors in a buyer’s product evaluation, website or brand reputation also contributes to perceived product quality, which indirectly plays a role in encouraging continued intention to use e-commerce (Sullivan & Kim, 2018; Wang, 2013).

In ECT, buyer’s product evaluation explains the balance between consumer expectations and product/service performance to predict customer satisfaction and repeat purchase intentions (Shang & Wu, 2017). Customers will evaluate perceived value by comparing expectations built during pre-purchase and product/service performance during post-purchase. Several things that consist of customer evaluation are comparing the quality of the product/service and the price or costs the customer must pay to get the product/service (Cho et al., 2019). In addition, customer expectations often grow due to brand reputation or online reviews appearing in digital media. To prove actual product/service performance, testing the gap between pre-purchase expectations and actual post-purchase performance is necessary. If customer expectations exceed performance, customers can experience post-purchase dissatisfaction and vice versa. Therefore, this research applies ECT in the buyer’s evaluation model to predict satisfaction and repurchase intention.

ECT in logistic service quality

In the context of ECT, service quality (servqual) measures how much a service meets or exceeds customer expectations. It is considered an essential strategy for the success and survival of a company in a competitive environment (Zeithaml et al., 1996). The Servqual model is applied to service industries, such as restaurant businesses, hotels, grocery stores, and others. However, along with the development of digital services in the service industry, Parasuraman et al. (2005) introduced the concept of service quality in the context of electronic services, which they termed as ‘e-service quality’ (E-S-Qual). This term refers to the quality of service provided by a website or online platform in delivering services to customers. This concept covers various aspects, such as speed, ease of use, reliability, responsiveness, and privacy (Rafiq et al., 2012). This model has been widely adopted in the e-commerce business.

Rafele (2004) adapted the Servqual model for distribution services by categorizing logistics service quality into three dimensions: 1) Tangible: Resources utilized to provide the service, including assets, personnel, and product availability. 2) Fulfilment: Reliability, responsiveness, and friendliness in the delivery process such as flexibility, speed, delivery system, timeliness, and service care. 3) Informative action: Product information, descriptions, ordering procedures, payment confirmation, receipt of goods, customer complaint services, and return services, as well as exchange of goods. The logistics service quality model in e-commerce varies for long-lasting products and fresh produce. General e-commerce with long-lasting products like fashion, cosmetics, electronics, and other general items places less emphasis on timely storage, packaging, and delivery logistics. In contrast, fresh produce e-commerce requires specific strategies to ensure proper packaging and timely delivery to maintain product freshness.

Then, García et al. (2021) explained the various aspects to build logistic strategy for e-grocery consist of reliability, responsiveness, guarantees, empathy, physical logistics facilities, completeness and accuracy of information, product safety, operational efficiency, waiting time, delays, order and delivery management, flexibility, product availability, and logistics waste management. The quality of logistics services is not just matter of customer satisfaction, but a key determinant of a business’s success and competitiveness in the market. As Subramanian et al. (2014) argue, a robust logistics strategy is essential for online purchasing services, particularly in sectors like e-grocery where stringent demands for temperature control, tracking, and product quality exist. The choice of a warehouse, for instance, can significantly impact a company operations, offering advantages such as multi-order and delivery service management, real-time product information, and availability (García et al., 2021; Sturiale & Scuderi, 2019).

Hypothesis development

Confirmation

Confirmation, is a key determined in customer satisfaction, is established through customers’ confirmation of service performance and the original expectation (Oliver, 1980). Specifically, Bhattacherjee (2001) defined confirmation as the user’s perception of the conformity between expectations and the actual performance of an information system. When the experience confirms or exceeds the expectation, confirmation occurs, which directly leading to the realization of customer satisfaction (Brown et al., 2012; Thong et al., 2006). Similar studies of Hadji and Degoulet (2016) and Jumaan et al. (2020) stated that confirmation is a psychological condition when the experience of using a service can explain that the actual performance is commensurate with a person’s previous expectations, which will directly increase acceptance of the benefits (perceived usefulness) and user satisfaction. In addition, performance confirmation demonstrates initial adoption belief, which can enhance the perceived usefulness of information system services in e-stores (Halilovic & Cicic, 2013; Wu et al., 2020).

Additionally, the confirmation of the information system’s performance has a direct impact on the quality of electronic logistics services. It’s worth reiterating that an e-commerce application page can only effectively present product information, confirm orders and payments, and provide real-time and accurate delivery times if it is backed by a reliable information system performance (Mahbubulhye et al., 2020; Nadeem et al., 2018). This underscores the urgency of maintaining the ICT performance reliability. As Leyerer et al. (2020) underline, a smart city with a well-established ICT infrastructure can furnish precise e-tracking information on the mobile application screen, thereby hastening last-mile e-grocery services in densely populated city areas. Accordingly, the following hypotheses are developed:

H1a. The confirmation of application performance has a positive effect on perceived usefulness.

H5. The confirmation of application performance has a positive effect on e-logistics service quality.

Perceived usefulness

Davis et al. (1989) defined perceived usefulness as an individual believing that using information technology will improve job performance. Bhattacherjee (2001) assessed that perceived usefulness is more consistent than perceived ease of use in influencing satisfaction at the post-acceptance stage. So, the EC-TAM model uses perceived usefulness to predict the continuance intention of information systems (Hsu & Lin, 2015). Wen et al. (2011) and Shang and Wu (2017) explained that perceived usefulness describes consumers’ perceptions of website performance in improving the online shopping experience. Halilovic and Cicic (2013) measured perceived usefulness based on the high ability of software to improve performance while increasing user satisfaction. Jumaan et al. (2020) measured perceived usefulness based on the ability of mobile internet to complete work faster so that users feel more satisfied and intend to use mobile internet sustainably. However, this differs from Sreelakshmi and Prathap (2020) who explained perceived usefulness based on the benefits of mobile-based payments in preventing possible virus transmission during the COVID-19 pandemic. Accordingly, the following hypotheses are developed:

H1b. The perceived usefulness of the application has a positive effect on users’ satisfaction.

Reputation

Reputation is generally a result of an overall assessment of the website’s product and service expertise, consumer experience, and effective communications about the website’s credibility in serving consumers (Li, 2014). Reputation is a signal of quality (Wells et al., 2011). Therefore, companies are generally willing to spend large amounts of money to advertise their products, which implies the company’s reputation behind the emergence of a quality product. A marketplace’s reputation describes its ability to provide high-quality products and perfect service (Li et al., 2020; Sullivan & Kim, 2018). Online product reviews strongly incentivize consumers to make purchasing decisions (Alalwan, 2020). When an e-store’s reputation is built, consumers will automatically have a positive picture of product quality, encouraging customer satisfaction and increasing customer interest in making repeat purchases on an electronic shopping service (Myunghee & Miyoung, 2017; Shin et al., 2013). Even Qalati et al. (2021) stated that a company’s reputation can reduce customer risk perceptions, both from the perspective of product quality and service quality (including the quality of delivery services). Similar to the statement of Vakulenko et al. (2019), the online shopping experience customers feel when interacting with e-commerce application pages will provide an overview of the company’s reputation for providing reliable and timely delivery services. Moreover, Kim et al. (2019) also said that brand prestige impacts customers’ perceived value of products and encourages customer loyalty in grocery stores. Accordingly, the following hypotheses are developed:

H2a. The brand reputation has a positive effect on perceived product quality.

H3a. The brand reputation has a positive effect on users’ satisfaction.

H6. The brand reputation has a positive effect on e-logistics service quality.

Perceived price fairness

Price fairness is one factor influencing someone to make purchasing decisions. Dodds et al. (1991) and Sullivan and Kim (2018) stated that price often determines perceived product and service quality. In addition, De Toni et al. (2018) also stated that perceived price fairness influences the perceived value of a service. Consumers tend to compare the price paid to obtain services. This balance between price and benefits is what is meant by price fairness (Asti et al., 2021). The price fairness measurement includes how the price offer can benefit customers financially. In other words, price fairness is the suitability between the price paid by consumers to obtain products and services and the quality of the products and services received. Beneke et al. (2013) argued that the relative price customers feel when purchasing private-label goods affects the perceived quality of the products. Similarly, Zhang et al. (2018) explained that price perceptions play a role in determining product quality, particularly in supermarkets offering organic vegetable products. The higher price felt by customers is reflective of the quality of the vegetables they receive. Additionally, Ali and Bhasin (2019) and Wang et al. (2019) stated that perceived price not only influences perceived value, but also affects customers’ satisfaction, indirectly influencing their intention to repurchase. Accordingly, the following hypotheses are developed:

H2b. The perceived price fairness has a positive effect on perceived product quality.

H3b. The perceived price fairness has a positive effect on users’ satisfaction.

Perceived product quality

According to Zeithaml (1988) perceived quality is the result of consumer assessment of the overall quality of the product. The characteristics of perceived product quality include: it is different from the actual quality, a higher-level abstraction rather than a specific attribute of a product, a global assessment that, in some cases, resembles attitude and a judgment usually made by a consumer. Then, Kaswengi and Lambey-Checchin (2019) explained perceived quality as a form of perceived product freshness, product quality in general, and logistic service quality. Likewise, Konuk (2019) mentioned perceived product quality as part of perceived quality. At the same time, Tian et al. (2022) stated that food quality, brand image, information quality, and promotional efforts are part of perceived quality, which stimulates millennial consumers to intend to repurchase organic tea. Other research stated that the better the quality of product freshness, the more influence it will have on customer satisfaction when shopping online for fresh products (Guan et al., 2022; Hafez et al., 2021; Wang et al., 2019). Accordingly, the following hypotheses are developed:

H3c. The perceived product quality has a positive effect on users’ satisfaction.

Logistic service quality

In electronic commerce, Jain et al. (2021) delineated key dimensions of e-logistic service quality as comprising product availability, timeliness delivery, and product condition play a pivotal role in augmenting satisfaction levels and cultivating intentions for subsequent online shopping engagement. Another opinion stated that logistics service quality increases operational flexibility and competitive advantage, underlining the importance of service quality factors in achieving business success in various sectors (Ali et al., 2022). In a complementary vein, Kaswengi and Lambey-Checchin (2019) asserted the indispensable significance of packaging quality within the logistic service framework. Emphasizing the critical role of logistic service quality in safeguarding perishable product, the findings consider that the enhancement of packaging quality indirectly contributes to heightened customer satisfaction, manifesting in favorable responses such as increased shopping cart and shopping frequency.

Furthermore, Mofokeng (2021) accentuated the centrality of delivery service within the e-commerce logistic experience. The findings of the studies underscore the substantial and positive impact of electronic logistics services on overall shopping satisfaction, consequently influencing the propensity to engage in repeat transaction with online shopping services. Thus, enhancing the quality of e-logistics services emerges as a key strategy to elevate customer satisfaction levels and strengthen customer loyalty. Accordingly, the following hypotheses are developed:

H7. The e-logistics service quality has a positive effect on perceived product quality.

H8. The e-logistics service quality positively affects the continuance intention to use fresh produce applications.

User satisfaction

Satisfaction is the perceptual interaction between expectation level and confirmation (Oliver, 1999). When a user’s expectations are met or exceeded during their actual experience with the technology, it confirms their satisfaction and intention to continue using the technology or service (Hadji & Degoulet, 2016; Jumaan et al., 2020). In the realm of electronic service, user satisfaction fuels growing commitment and loyalty, leading to repeat use (Mamun et al., 2020; Shin et al., 2013). Other study found that e-store performance confirmation and perceived usefulness influence technological performance, enhancing customer satisfaction and promoting online impulse buying (Wu et al., 2020). Consequently, customer confirmation of service quality becomes crucial for predicting future behavior (Aparicio et al., 2021). User satisfaction determines customers’ happiness with a company’s products, services, and capabilities (Mofokeng, 2021). Accordingly, the following hypotheses are developed:

H4. The users’ satisfaction has a positive effect on continued intention to use fresh produce application

Building on the foregoing discussion, researchers underscore the imperative of incorporating the Expectation-Confirmation Theory (ECT) into an integrated model that encompasses technology acceptance, product evaluation, and electronic logistics service quality (Park, 2020). This strategic integration is deemed essential for the sustainable development of an e-retail business focused on fresh, perishable products. The synergy of these three theories is critical in fostering consumer persistence and continued utilization of the service. Furthermore, this study presents the conceptual model in Figure 1.

Figure 1. Proposed conceptual model.

Method

Sampling and data collection

This study uses a quantitative approach. The steps in this research are problem formulation, literature review, hypothesis formulation, conceptual model formulation, instrument design, instrument validity and reliability testing, data collection, data processing, and power analysis. Data was collected by distributing questionnaires online via social media from August to October 2022. Initially, researchers confirmed participants’ willingness to fill out the questionnaire. The anticipated time commitment for completing the questionnaire was approximately 15 minutes.

The study was conducted with unwavering adherence to the ethical standards set forth in the 1964 Declaration of Helsinki and its later amendments. The Institutional Review Board of Universitas Negeri Jakarta, recognizing this commitment, granted ethical approval for this study. All participants gave informed consent for participation in the study. Written consent was secured to ensure thorough documentation and participant understanding. Before responding to the research questionnaire, participants were provided with detailed information about the study, including its purpose, procedures, potential risks, and benefits.

This study employs the purposive sampling technique to screen respondents based on predetermined criteria, aiming to analyze the behavior of users in fresh produce e-commerce. This sampling approach is particularly advantageous for an infinite population and emphasizes the inclusion of diverse perspective (Taherdoost, 2016). Numerous emerging fresh produce e-commerce platform in Indonesia including Sayurbox, Carisayur, and Segari, have become viable alternatives for majority of urban citizen, particularly in Jakarta (Agarwal & Jain, 2022). Hence, to examine the determinants influencing continuance intention, it is imperative that respondents be residents of Jakarta who have actively engaged in fresh produce e-commerce within the preceding six months.

The number of participants in this online survey was 396, but those who met the criteria required in this research were 364 respondents. Hair et al. (2019) determine an adequate sample size for processing data through structural equation modeling requires a sample exceeding 200 or ranging from 5 to 10 times the number of measurement items. In this study, with a total of 56 items, the sample size followed the specified requirements, thus ensuring the robustness of the data analysis within the structural equation modeling framework.

From the data collection results, data processing in this study used Covariance-based Structural Equation Modeling (CB-SEM) using a valid copyright license for AMOS (Analysis of Moment Structures) version 24 software tool. Hair et al. (2019) explained that CB-SEM is a statistical method for testing and modeling the relationship between variables in one conceptual model. CB-SEM aims to check the extent to which observational data fits the model. So, several steps in CB-SEM include variable measurement, determining the conceptual model, testing construct (instrument) validity, testing model fit, hypothesis testing, and parameter estimation.

Questionnaire design and measurement

The questionnaire is structured into distinct sections. The initial segment comprises screening questions designed to select participant eligibility. Subsequently, the second section inquiries regarding the respondent’s profile. Finally, the third segment consists of statements crafted to evaluate the respondent’s behavioral patterns in utilizing fresh produce e-commerce.

The measurement scale used in this research is a 5-point Likert scale (point 1 to express "Strongly Disagree" to the item, to point 5 to state "Strongly Agree" to the item). This research has tested seven variables that influence repurchase intention, both directly and indirectly, in a structural model. E-LSQ and customer satisfaction directly influence repurchase intention, while reputation, price perception, product quality, confirmation, and perceived usefulness indirectly influence repurchase intention. In the EC-TAM model, two variables influence customer satisfaction and intentions to use electronic services continuously: confirmation and perceived usefulness. To measure customer confirmation of electronic services based on five measurement items that describe the suitability of expectations and actual technology performance based on customer experience (Jumaan et al., 2020). Furthermore, measuring the perceived usefulness of e-grocery applications consists of seven items describing how e-grocery applications can support customer shopping activities and save shopping time (Jain et al., 2017; Sullivan & Kim, 2018).

Next, E-LSQ is a mediating variable that plays a role in informing product quality, managing orders and deliveries, and a customer relations medium so that through good E-LSQ, customers can receive good product quality, too. The E-LSQ second order measurement includes logistics information services, ordering and delivery management services, and customer relationship services. So, the total number of items is 15 (Jain et al., 2017; Rafele, 2004; Rafiq et al., 2012). Furthermore, three variables in the buyer’s product evaluation influence satisfaction and intention to use the service on an ongoing basis: reputation, price perception, and product quality perception. Reputation depicts the company/brand’s image to its consumers. There are six items to measure the perceived reputation of e-grocery applications, which describe the popularity of a brand, the level of user reviews, and brand credibility (Jarvenpaa et al., 2006). Perceived price is a description of the suitability of the price level with the benefits obtained by customers, where there are five measurement items. E-logistics service quality has six measurement items (Asti et al., 2021; Gefen & Devine, 2001; Sullivan & Kim, 2018). Perceived product quality is a description of the conformity of the quality of the product ordered with that received. Product quality measurement consists of 6 items, including product freshness, product integrity, and product safety for consumption (Asti et al., 2021; De Toni et al., 2018).

Pilot study

To assess the validity and reliability of the research instrument, the researchers involved 50 participants, including researchers in the field of digital marketing to evaluate the questionnaire design. Malholtra et al. (2016) explained several steps in testing constructs validity, namely: ensuring that each measurement item on a variable has validity with a loading factor value showing a number above 0.5; ensure that all items or variable constructs have high reliability with a Cronbach’s Alpha value above 0.7; and ensure that each variable in the structural model has a correlation level that does not exceed 0.8. In other words, the structural model shows no multicollinearity, or each variable in the structural model does not have a high level of similarity, thereby preventing biased analysis results. In addition, regulation must be considered to increase the continuance intention in young consumers follow (Shrestha, 2020).

The research instruments and results of validity and reliability tests on the constructs were carried out using SPSS 24 software and are presented in Table 1.

Table 1. The research instruments and the result of construct validity.

Construct	Loading factors	Cronbach’s alpha	References
Reputation			Jarvenpaa et al. (2006) – 6 items
REP1. The e-grocery application is well-known.	0.661	0.815
REP2. The application has a favorable rating	0.751
REP3. The application’s online reviews were credible.	0.760
REP4. The online reviews were helpful for me to evaluate the product.	0.733
REP5. The online reviews of e-grocery apps were helpful for me to evaluate the service quality.	0.735
REP6. The application has a good reputation among its consumers.	0.749
Perceived Price Fairness			Asti et al. (2021)– 5 items
PRI1. Items found in e-grocery application are sold at reasonable prices.	0.628	0.735
PRI2. The price of the product on this application is affordable.	0.647
PRI3. I might save more money if I shop on an e-grocery application.	0.718
PRI4. The products’ price on e-grocery application is cheaper, compared to others.	0.747
PRI5. It may be possible to get a better discount from this application.	0.740
Perceived product quality			Asti et al. (2021) – 6 items
PRO1. E-grocery application offers a variety of products.	0.623	0.827
PRO2. The products offered at e-grocery are high quality.	0.717
PRO3. Products delivered are intact condition.	0.759
PRO4. Products delivered are fresh.	0.780
PRO5. Products delivered are edible.	0.768
PRO6. Overall, product has a good quality.	0.749
Confirmation			Jumaan et al. (2020)– 5 items
CON1. Based on my experience, I found that using mobile application was better than what I expected.	0.753	0.804
CON2. I found it easy to download the mobile application.	0.785
CON3. I’ve never had trouble accessing the mobile application.	0.691
CON4. In my opinion, this mobile app is user-friendly.	0.745
CON5. Overall, most of my expectations from using the mobile internet were confirmed.	0.771
Perceived usefulness			Jumaan et al. (2020)– 7 items
USE1. The mobile application improved my performance in shopping.	0.806	0.852
USE2. The transaction process on the mobile application enhanced my effectiveness in purchasing the product.	0.778
USE3. Shopping on mobile application was saving my time.	0.628
USE4. I think the mobile application was very useful in purchasing the product	0.754
USE5. I can shop anytime when I used the mobile application	0.778
USE6. The mobile application provided home-delivery service that support my activities.	0.736
USE7. The mobile application provided digital payment that makes my shopping becomes more practice.	0.631
Satisfaction			Jumaan et al. (2020)– 7 items
SAT1. Satisfied with mobile application service quality.	0.748	0.838
SAT2. Satisfied with product quality.	0.615
SAT3. Satisfied with product availability.	0.798
SAT4. Satisfied with timeliness delivery.	0.809
SAT5. Satisfied with shopping condition.	0.766
SAT6. Satisfied with shopping payment.	0.718
SAT7. Overall, I satisfied when shopping with this mobile application	0.628
Continuance intention			Jumaan et al. (2020)– 5 items
CIN1. If I were to buy the groceries, I would likely to buy it from the same mobile application.	0.643	0.815
CIN2. If I could, I would like to reuse to the mobile application for my next purchases.	0.711
CIN3. I Intend to revisit the mobile application in the future.	0.807
CIN4. I would like to revisit the mobile application in the near future.	0.822
CIN5. I always revisit the mobile application when I want to shop.	0.799

Instrument development of e-logistic service quality uses the direct oblimin method in SPSS 24 to rotate items and group them into dimensions based on the similarity of item characteristics. The 14 e-logistics service quality items are divided into information services, customer relations, and order management and fulfillment. This division is not arbitrary but is based on the scientific work of Rafele (2004), who developed a distribution channel service framework combined with the ES-QUAL framework of Parasuraman et al. (2005) and Rafiq et al. (2012). To ensure the instrument, this study employs validity and reliability tests by SPSS 24. The second level construct of e-logistics service quality, presented in Table 2, is a testament to the academic rigor of this research.

Table 2. The research instruments of e-logistic service quality and the result of second order construct validity.

	Second order construct
Pattern matrix of e-logistic service quality	1	2	3	Cronbach’s alpha	References
Information services:				0.882	(Parasuraman et al. 2005; Rafele 2004; Rafiq et al. 2012)
LOG1. The application informs the product availability.	0.842
LOG2. The product classification on the e-grocery application makes it easy to find what I need.	0.768
LOG3. The product’s description enables me to get the product details.	0.768
LOG4. The application gives me the flexibility to choose time of delivery.	0.762
LOG5. I have received prompt service from the online stores.	0.751
LOG6. The application informs the transaction process and payment.	0.556
Customer relationship:
LOG7. The e-grocery application provides an accessible customer care online.		0.698
LOG8. The e-grocery application shows a sincere interest in solving customer problems.		0.696
LOG9. The e-grocery application is always willing to inform if time of delivery is out of schedule.		0.674
LOG10. I have received prompt service from the online stores.		0.670
Order managing and fulfilment:
LOG11. The personnel are capable to safe the product during shipping time.			0.789
LOG12. Personnel of delivery are responsible deliver the products.			0.770
LOG13. The product’s well packaged.			0.719
LOG14. The company delivers products within suitable time frame.			0.616
Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalization.

Furthermore, the Pearson correlation test results to evaluate the multicollinearity of conceptual framework are presented in Table 3.

Table 3. The result of Pearson correlation testing.

n (364)	REP	PRI	PRO	ELSQ	CON	USE	SAT	CIN
Mean	4.423	4.420	4.44	4.37	4.37	4.411	4.38	4.44
Std. Deviation	0.391	0.350	0.374	0.362	0.399	0.392	0.349	0.404
REP	1	–	–	–	–	–	–	–
PRI	0.428**	1	–	–	–	–	–	–
PRO	0.481**	0.498**	1	–	–	–	–	–
ELSQ	0.537**	0.467**	0.608**	1	–	–	–	–
CON	0.482**	0.382**	0.529**	0.729**	1	–	–	–
USE	0.539**	0.376**	0.543**	0.764**	0.780**	1	–	–
SAT	0.490**	0.491**	0.533**	0.565**	0.545**	0.531**	1	–
CIN	0.381**	0.297**	0.463**	0.582**	0.498**	0.548**	0.530**	1

Note: REP (brand reputation), PRI (price fairness), PRO (product quality), ELSQ (e-logistic service quality), CON (confirmation), USE (perceived usefulness), and CIN (continuance intention). ** is significant at 5% level

Finding and discussion

Description of respondents’ profile

Based on the processing of 364 respondents’ data, the respondents’ socio-demographic data is presented in Table 4.

Table 4. Socio-demographic of respondents.

Socio-demographic items	Frequency	Percentage
	n = 364
Gender
Male	113	31.0
Female	251	69.0
Age
25–30	165	45.4
31–40	93	25.5
41–50	90	24.7
Above 50	16	4.4
Marital status
Single	129	35.4
Married	223	61.3
Divorced/widowed	12	3.3
Members of household
1 member	75	20.6
2 members	79	21.7
3 members	114	31.3
More than 3 members	96	26.4

The information in Table 1 explains that users of e-grocery shopping services, in terms of gender, are dominated by female customers, and the majority are married. In line with the results of a study from Monoarfa et al. (2023) dan Van Droogenbroeck and Van Hove (2019), who found that women tend to be more interested in adopting fresh produce applications because of their role as housewives or family shopping decision-makers.

Likewise, Agarwal and Jain (2022) also stated that fresh produce application customers are individuals and new young families with young children who generally do not have much time to leave the house for traditional grocery stores. Contrary to Kvalsvik (2022), which stated that fresh produce application customers are older adults with mobility issues and are far from home and grocery stores. The characteristics of users of the fresh produce application service show customer segmentation. As Van Droogenbroeck and Van Hove (2017) stated, personal and household characteristics strongly influence consumer preferences in choosing alternative shopping services for fresh food products. Furthermore, socio-demographic respondents show that the users of fresh produce e-commerce are generally in the age range 25 to 30 years. Even though the age range of 31 to 50 years, they also used fresh produce e-commerce to meet their needs for fresh food. The result is similar to the Ken Research survey that online shopping service customers are 23 to 54 years old and work as employees or professionals (Sanjeev, 2023).

Confirmatory factor analysis

Confirmatory factor analysis within the structural equation modelling entails the examination of the goodness of fit of the structural model. The goodness of fit model testing aims to ensure that all constructs in the structural model meet the model fit requirements (Hair et al., 2019). There are several ways to make the structural model fit, namely: 1) modifying the construct by eliminating items that have high modification index (M.I) values; 2) adding correlation between constructs as suggested by the processing system in the SEM-AMOS 24 software. The modification results have produced a fit model that meets the requirements as presented in Table 5.

Table 5. The result of goodness of fit model.

Fit indices	Cut-off value	Value	Result
Chi-square	Minimum	376.369	Fit
Probability	>0.05	0.028	Fit
CMIN/DF	<2.00	1.115	Fit
RMSEA	<0.08	0.021	Fit
GFI	>0.90	0.933	Fit
AGFI	>0.90	0.917	Fit
TLI	>0.95	0.982	Fit
CFI	>0.95	0.984	Fit

In the next stage, after the structural model is declared fit, SEM-AMOS 24 processes and analyses the data to test the proposed hypothesis. The results of data processing and analysis are presented in Figure 2 and Table 6.

Figure 2. The fit model of conceptual framework.

Table 6. Result of hypothesis testing.

Path hypothesis		Coefficient (β)	Critical ratios (C.R)	Prob. value (ρ)	Result
H1a.	CON → USE	0.993	8.824	0.000	Supported
H1b.	USE → SAT	0.329	3.330	0.000	Supported
H2a.	REP → PRO	0.241	2.214	0.027	Supported
H2b.	PRI → PRO	0.250	3.185	0.001	Supported
H3a.	REP → SAT	0.224	1.956	0.049	Supported
H3b.	PRI → SAT	0.153	3.170	0.048	Supported
H3c	PRO → SAT	0.285	2.319	0.020	Supported
H4.	SAT → CIN	0.330	3.108	0.002	Supported
H5.	CON → E-LSQ	0.827	7.131	0.000	Supported
H6.	REP → E-LSQ	0.216	2.200	0.028	Supported
H7.	E-LSQ → PRO	0.386	4.426	0.000	Supported
H8	E-LSQ → CIN	0.494	2.670	0.008	Supported

Note: REP (brand reputation), PRI (price fairness), PRO (product quality), ELSQ (e-logistic service quality), CON (confirmation), USE (perceived usefulness), and CIN (continuance intention). n = 364. C.R value ≥ 1.96 and significance level of ρ < 0.05.

An overview of the magnitude of the direct influence between variables in the structural model is presented in Figure 3.

Figure 3. Direct effect on structural model. Notes: *, **, and *** = significant at level 10%, 5% and 1%.

Discussions

Moreover, based on the results of the hypothesis test the overall value of critical ratios (C.R) in the path causality relationship shows a value that is above 1.96 as the required value based on an error limit of 5%. Likewise, each causality relationship’s probability value is below 0.05, indicating that the proposed hypothesis is accepted. Based on Figure 3, E-LSQ and customer satisfaction are critical factors for predicting continued intention to use fresh produce e-commerce. E-LSQ has a more substantial effect on predicting continuous intentions with a coefficient of 0.503 compared to user satisfaction. This result is similar to Jain et al. (2021) that E-LSQ, which consists of the dimensions of availability, timeliness, and condition, has an essential influence on online repurchase intention. In contrast, Cao et al. (2018) and Kaswengi and Lambey-Checchin (2019) argued that E-LSQ directly influences customer satisfaction and indirectly influences repurchase intention.

Various previous studies state that E-LSQ and customer satisfaction encourage the growth of intention to continue using fresh produce e-commerce. For this reason, this research explores factors that can support e-logistics service quality and increase user satisfaction. Several factors that support E-LSQ include confirmation of application performance and brand reputation. The research results show that confirmation of application performance strongly influences E-LSQ with a coefficient value of 0.855. At the same time, Nadeem et al. (2018) said that the performance of information and communication technology contributes to e-logistics performance. Likewise, Lin (2022) stated that I.T. resources and I.T. quality contribute to implementing e-supply chain services, including integrated internal and external collaboration services. Several studies prove that confirmation of technological performance is vital in E-LSQ. The performance of technology must be reliable and integrated to provide accurate and real-time logistics information services, systematic order management and delivery services, and interactive customer relationship services.

The reliability of I.T. not only plays a crucial role in the E-LSQ, but also enhances the perceived usefulness and satisfaction of using fresh produce e-commerce. Thong et al. (2006) dan Brown et al. (2012) demonstrated the alignment between expectations and the actual experience significantly influences user satisfaction, then fostering continued intention to use I.T. Additionally, Jumaan et al. (2020) asserted that users, upon experiencing the advantages of mobile internet services, also derive optimal benefits. This underscores the importance for fresh produce e-commerce platforms to possess robust information systems capable of presenting diverse service features and delivering maximum benefit to customers. Various perceived usefulness of online shopping for fresh products align with consumer preferences, encompassing time-saving, shopping effectiveness, flexibility, and other conveniences not readily available through physical store (Maltese et al., 2021; Monoarfa et al., 2023; Singh, 2019).

Besides confirming technological performance, this study found that brand reputation influences E-LSQ. Buyer’s product evaluation explains that for consumers, brand reputation provides their perception regarding product quality and service quality. Generally, a website with a good reputation offers reliable product quality and excellent service (Sullivan & Kim, 2018). In more detail, Hu et al. (2022) argue that website reputation impacts E-LSQ, further contributing to increasing customer satisfaction and loyalty.

In addition, user satisfaction also predicts continued intention. Thus, this research has explored what factors can encourage user satisfaction with fresh produce application services. The results found that perceived usefulness is essential to user satisfaction, with a coefficient value of 0.355. This finding aligns with the previous study of Droogenbroeck and Van Hove, (2021). Through the implementation of the unified theory of acceptance and use of technology (UTAUT), the perceived usefulness has provided a more decisive impetus in increasing satisfaction compared to other factors in UTAUT. Moreover, the results have found that product quality also plays a role in increasing customer satisfaction. Kaswengi and Lambey-Checchin (2019) stated that product quality, which is reviewed based on freshness, is the primary key to customer satisfaction using fresh food drive-through services. In contrast, Asti et al. (2021) and Konuk (2019) argued that food quality is essential in increasing satisfaction. However, price fairness also significantly impacts perceived value and satisfaction when shopping for fresh groceries and organic food.

The research results show that brand reputation increases user satisfaction. Consumers consider that reputation reflects the quality of products or services. As Chakraborty (2019) explained that customers online reviews indirectly describe the quality of products/services as a result of evaluating the level of satisfaction. For this reason, Singh and Söderlund (2020) stated that positive customer experiences regarding brand reputation have a higher impact on satisfaction, so customers tend to intend to repurchase and use word-of-mouth to share their shopping experiences with friends/family.

Furthermore, this research also analyses the magnitude of the influence on each path causality relationship, both directly and indirectly, and the role of mediating variables in providing a more significant influence on endogenous variables. An overview of mediating variables is presented in Table 6.

Based on Table 7, the causal relationship between confirmation of e-commerce application performance and customer satisfaction fully requires the mediating role of perceived usefulness. The quality of the information system predicted that the application would provide usefulness that may increase shopping effectiveness (Jumaan et al., 2020; Sreelakshmi & Prathap, 2020). Likewise, the E-LSQ causality relationship must be balanced with good perceived product quality to achieve maximum customer satisfaction. Logistics information on the e-commerce page must encourage an appropriate order management and delivery system, a packaging system, and timely delivery. So, the products’ quality presents freshness and edibility that meet customer expectations and satisfaction (Jain et al., 2021; Mofokeng, 2021).

Table 7. The result of mediating effect.

Path	Direct effect	Indirect effect	Total effect	Result
CON → USE → SAT	0.000	0.413	0.413	Full mediation
E-LSQ → PRO → SAT	0.000	0.098	0.098	Full mediation
CON → E-LSQ → CIN	0.000	0.482	0.482	Full mediation
REP → E-LSQ → CIN	0.000	0.160	0.160	Full mediation
USE → SAT → CIN	0.000	0.114	0.114	Full mediation

Note: REP (brand reputation), PRI (price fairness), PRO (product quality), ELSQ (e-logistic service quality), CON (confirmation), USE (perceived usefulness), and CIN (continuance intention). n = 364. Significance level of < 0.05.

Furthermore, the role of E-LSQ in mediating the causal relationship between confirmation of e-commerce performance and brand reputation on continuity of intention to use e-commerce is crucial. In short, the e-commerce performance must support the logistics management system (Nadeem et al., 2018). The order and delivery management system will be well-processed when the product information on the e-commerce page is accurate, real-time, and integrated into the warehouse information system. Then, the customer will accept the ordered items on time. Timeliness delivery is critical to ensuring the product’s freshness (Jain et al., 2021; Kaswengi & Lambey-Checchin, 2019; Mofokeng, 2021). A pleasant and satisfying shopping experience will tend to increase the intention to use fresh produce e-commerce (Kaswengi & Lambey-Checchin, 2019).

Based on mediation effect analysis, this research considers the importance of predicting causal relationships between latent variables in a construct by analyzing the model’s determination value (R-square) (Sekaran & Bougie, 2016). The ability of variables in the structural model in terms of their squared multiple correlation values is classified into several levels, namely R-square ≥ 0.67 (strong), R-square ≥ 0.33 (medium), and R-square ≥ 0.19 (weak).). The results of data analysis via AMOS 24 obtained estimated values for squared multiple correlation (R-square) as follows: perceived E-LSQ (0.826), perceived product quality (0.656), perceived usefulness (0.970), and satisfaction (0.616).

Among several variables that influence the continuity of intention to use fresh produce e-commerce, perceived usefulness and E-LSQ significantly contribute to the structural model. It only takes one confirmation of information system performance to increase perceived usefulness as a predictor of continuance intention. Then, the contribution of E-LSQ to continuance intention depends on two factors, namely confirmation of information system performance and perceived reputation, which actually play a small role in this case (as can be seen from the coefficient value in Table 2). Likewise, there are many factors in the buyer’s evaluation (perceived product quality, price fairness, and reputation) to encourage customer satisfaction’s role in continuance intention, and it takes more efforts and strategy for the company to optimize customer satisfaction to improve continuance intention to use fresh produce e-commerce. However, the latent variables have a strong predictive ability for continued intention to use fresh produce e-commerce.

Conclusion

Based on discussion and development in the fresh produce retail business, analysing the factors that influence the continued intention to use fresh produce e-commerce has intense urgency. The findings are crucial for business sustainability and instrumental in answering the first research objective of identifying potential customers for fresh produce e-commerce services. It is interesting to note that a significant number of women use fresh produce e-commerce services to meet family consumption needs. However, it is worth exploring whether some independent men in urban areas also engage in this practice. Most users are young individuals or new families who, as city dwellers, tend to have a digital lifestyle.

Addressing the second research objective, this study concludes that among several integrated theories to predict continuity of intention to use fresh produce e-commerce, the contributions of perceived usefulness, E-LSQ, and perceived product quality are significant. It is crucial for e-commerce to have a technology performance that can enhance customers’ perceived usefulness, leading to increased user productivity and customer satisfaction. From the perspective of implementing EC-TAM, satisfaction is one of the predictors of intention to reuse services in the future. For this reason, service user confirmation measurements state that technology performance significantly influences customer acceptance of the benefits of e-commerce shopping applications. Apart from that, this research also presents novelty by integrating the concepts of EC-TAM and buyer’s product evaluation, as well as positioning the importance of the role of e-logistics service quality to optimize fresh produce e-commerce services.

Furthermore, to answer the final research objective, it can be concluded that the convenience of logistics services is a key attraction of using fresh produce e-commerce shopping services. This service eliminates the need for customers to leave their homes to reach a grocery store, making it particularly suitable for city residents who often have a high level of mobility. Moreover, the quality of logistics services also plays a vital role in maintaining product freshness, which is supported by timely information, ordering, and delivery services. The findings of this research state that the position of e-logistics service quality is crucial to maintaining the quality of the products sent so that their freshness is maintained until customers receive them. E-LSQ is not only about the delivery process but integrates the logistics information system, the delivery process, and logistics services during the post-purchase period. A fresh produce e-commerce company needs to have a qualified information system. As the study results show that confirmation of technological performance provides a strong impetus for E-LSQ. The research further concluded that e-LSQ, directly and indirectly, influenced the continuity of intention to use fresh produce e-commerce.

Managerial implication

Apart from theoretically contributing, the findings of this research also contribute practically and empirically regarding the main factors that must be considered when marketing and designing strategies for the sustainability of the fresh produce e-commerce retail business. As the findings state, the importance of technology performance that supports perceived usefulness. Fresh produce e-commerce must ensure that service features can increase user satisfaction in terms of convenience in the online purchasing process. Compared with physical services, the advantage of online shopping services is the flexibility of shopping time, which can be done at any time to meet household consumption needs, especially for individuals or families who do not have a particular time to shop at physical markets.

The quality of technology in fresh produce e-commerce is not just about customer convenience. It also plays a crucial role in supporting the quality of electronic logistics services, particularly in presenting product and service information accurately. The information presented on the application’s home page sets customer expectations about the quality of products and services, such as product images and descriptions, ordering procedures, and delivery service information. The quality of electronic logistics services is instrumental in preserving product freshness. Given the perishable nature of fresh food products, they require specific packaging techniques and delivery facility support to ensure their quality is maintained until they reach the customers. As García et al. (2021), suggest, the value proposition offered by business actors can guide the formulation of logistics service strategies.

Some possible value propositions businesses can offer include delivery duration, schedule options, or determining the delivery area (Sturiale & Scuderi, 2019). In another opinion, Piroth et al. (2020) mentioned factors for the success of logistics and delivery services, including fast and timely delivery duration, which supports the products delivered being received in fresh condition. Apart from that, the varied delivery options also attract flexibility in online shopping. Meanwhile, restrictions on delivery areas play a role in supporting timely delivery. Delivery personnel will only focus on certain delivery areas. Meanwhile, increasing market share can be done by increasing the number of delivery hub points (Lagorio & Pinto, 2021).

In Indonesia, the market share for fresh products online is currently smaller than that for physical shopping, largely due to the local preference for the social aspects of physical market shopping (Agarwal & Jain, 2022). However, this presents an opportunity for fresh produce e-commerce companies to enhance their digital marketing efforts (Albors-Garrigos, 2020). By leveraging interactive communication channels through social media, they can provide customers with an informative and entertaining experience. Positive customer experiences and responses to online shopping can significantly enhance the company’s reputation, thereby stimulating other customers’ interest in making purchasing decisions. The research also highlights the role of brand reputation in fostering customer acceptance and trust in the quality of logistics products and services, which can further boost customer loyalty.

Likewise, price promotion strategies such as product bundling, shopping points, discount vouchers, or discount shipping vouchers are essential to increase sales. However, price attractiveness is still a relatively effective marketing strategy for increasing sales (Zhao & Bacao, 2020). However, the most important thing to consider is the conformity between price and product quality (perceived price fairness). Customers realize that shopping online will result in additional delivery costs. However, if delivery can be done on time and the product is fresh, additional delivery costs are not a big problem for customers busy in big cities. This means that the online shopping service has increased productivity and provided an effective and efficient shopping experience. If new e-commerce companies establish several strategies, this will indirectly increase repurchase intentions and customer loyalty. Relevant businesses could utilize the findings from this study to develop appropriate strategies by integrating specific technology features for improving shopping benefits and customer satisfaction, expanding their market, and building sustainable businesses for future development.

Limitation and future direction

Apart from presenting several findings that contribute to novelty, this research, of course, has limitations, such as the survey in this research only covers customers in the Jakarta area because fresh produce e-commerce services have only recently developed in several large cities in Indonesia, one of which is in Jakarta so that researchers have access to customer data. For this reason, the results of this research can be a reference for further research, both in Indonesia and in other cities in the Southeast Asia region, where the socio-demographic conditions of respondents are similar to Indonesia, where fresh produce e-commerce services have only become more popular during the Covid-19 pandemic.

Author contribution

All authors have made substantial contributions to the conception or design of the work, the acquisition, analysis, and interpretation of data for the work, and drafting the work or critically revising it for important intellectual content. Specifically: Terrylina A. Monoarfa: Contributed to the conception and design of the study, acquisition and interpretation of data, and drafting the manuscript. Ujang Sumarwan: Participated in the conception and design of the study and supervised the research. Arif Imam Suroso: Contributed to the interpretation of data, revised the manuscript critically for important intellectual content, and provided final approval of the version to be published. Ririn Wulandari: Involved in the conception and design of the study and supervised the research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors agree to make data and materials supporting the results or analyses presented in this paper available upon reasonable request. Author contact: [email protected]

Additional information

Funding

The authors received no direct funding for this research.

Notes on contributors

Terrylina A. Monoarfa

Terrylina A. Monoarfa is a lecturer at the Faculty of Economics Universitas Negeri Jakarta – Indonesia and currently a Doctor Candidate at the School of Business, IPB University, Indonesia. The research interest includes consumer behavior, technology adoption, and service quality.

Ujang Sumarwan

Ujang Sumarwan is a Professor at School of Business, IPB University, Indonesia. The research interest includes consumer behavior, marketing, and branding strategy.

Arif Imam Suroso

Arif Imam Suroso is a Professor at School of Business, IPB University, Indonesia. The research interest includes business intelligence, business analytics, and agricultural economics.

Ririn Wulandari

Ririn Wulandari is an Associate Professor at the Faculty of Economics and Business, Universitas Mercu Buana Jakarta, Indonesia. The research interest includes consumer behavior and marketing strategy.