Journal of Distribution Science (유통과학연구)

Korea Distribution Science Association (한국유통과학회)
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Gen Z's Intention to Repurchase Food Online in The Context of a Crisis: A Case Study in Vietnam Under COVID-19

  • Received : 2023.10.28
  • Accepted : 2023.12.05
  • Published : 2023.12.30
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Abstract

Purpose: The purpose of this study is to identify factors affecting satisfaction as well as the intention of Gen Z customers to reorder food online in the context of a crisis. Research design, data and methodology: Data for this cross-sectional study were collected via the Internet by conducting an online survey of 652 Gen Z respondents, aged 15-25, in the south of Vietnam and using a convenience sampling method. To analyse the reliability of the scales, SPSS was used to run Cronbach's alpha. Then, SmartPLS was used to assess the measurement model, including variable reliability and validity, convergent validity, and discriminant validity of the proposed model, as well as test the hypotheses with partial least squares structural equation modelling (PLS-SEM). Results: Social influence, price value, and convenience all have a positive effect on satisfaction and repurchase intention. Satisfaction not only plays a critical role in mediating the relationship between social influence, price value, convenience and repurchase intention but also has a positive impact on repurchase intention towards buying food online. Conclusions: This study was successful in identifying the factors of repurchase intention in a crisis setting among Gen Z customers by developing a theoretical research model via literature to complete a brief Theory of Planned Behaviour model. This study also took an innovative approach to earlier ones by demonstrating not just the significant effect of social influence on satisfaction and repurchase intention, but also by identifying critical variables that managers should focus on increasing and improving management.

Keywords

1. Introduction

There has been a boom in retail e-commerce and mobile e-commerce in the period 2020-2021; specifically, 2.8 billion people around the world utilized mobile applications, and the estimated e-commerce value of global retail sales reached $4.3 trillion of the total global retail market of $23.8 trillion by 2020 (Laudon & Traver, 2021), in which the value of e-commerce via social networks reached nearly 90 billion USD (Wire, 2020). However, online shopping has grown in popularity in developed countries, whereas developing countries, particularly those in Southeast Asia, have seen a lower prevalence of online shopping despite its high growth potential (Alyoubi, 2015; Kshetri, 2007). Recognizing the significance and contribution of e-commerce, policymakers and academics are working to understand and promote it (Bharadwaj & Soni, 2007; Burt & Sparks, 2003; Huang et al., 2022). Yet, the research context always changes over time and each study is only appropriate in a certain context. Recognizing the critical role of context in research, the author aims to study the online repurchase intention in the context of crises such as pandemics, wars and natural disasters.

Although the COVID-19 epidemic severely impacted the Vietnamese people in the second half of 2021, Vietnam effectively brought the epidemic under control through a widespread vaccination campaign and the effective implementation of epidemic prevention measures in 2022. During this period, the supply of essential food is sometimes insufficient to meet basic needs, and online purchasing services are recommended to be used to limit people's movement and thus lower the danger of disease spread. In the context of limited exposure to prevent COVID-19 in Vietnam, a number of companies or sellers have to change or add new methods of trading food, especially trading on online applications, in order to operate their trading effectively (Alvarez-Risco et al., 2022; Kumar & Kashyap, 2022; Nawangsari et al., 2020). In order for policymakers to have an insight into people in need of buying food several studies have been conducted to identify factors affecting customer satisfaction and repurchase intention (Singh et al., 2021) as well as investigate the relationship between customer satisfaction and repurchase intention (Qureshi et al., 2009). Nevertheless, there are still relatively few comprehensive studies on factors influencing satisfaction and intention to repurchase food through online applications in Vietnam (Nguyen Thi et al., 2022) in a crisis context. Hence, this is a practical requirement that needs to be researched in order to make accurate predictions about the determinants of repurchase intentions in the event of future crises.

Regarding the online repurchase intention, numerous studies were conducted with a variety of approaches (Bhatti et al., 2016; Hasan, 2021; Kazemi et al., 2013; Kim & Lee, 2019; Miao et al., 2022; Nguyen Thi et al., 2022) to explain the online repurchase intention as well as customer behaviour. Yet, the theory of planned behaviour (TPB) has been widely adopted by many authors when studying repurchase intention (Hasan, 2021; Lee et al., 2019). The TPB theory has the significant benefit of allowing the building of flexible models with the ability to include additional elements to increase its value for predicting certain behavioural scenarios (Zaremohzzabieh et al., 2019). TPB theory, however, presupposes a behavioural approach in one's environment that promotes intents and actions, while neglecting human processes and perceptions such as personality and result expectancies (Bandura, 2003; Miles, 2012). Thus, to explain the process of forming repurchase intention without considering external stimulus factors that influence attitude and intention leading to behaviour is an inadequacy. This is also a theoretical gap for building a better model of repurchase intention from the TPB.

Combining the urgent need for research during the crisis and the theoretical gaps above, this study clarifies the reasons why it is important to examine the determinants of repurchase intentions. With the development of the internet and the digital industrial revolution, all management and marketing principles have to adapt to correspond with the current context (Scott, 2009). However, identifying external factors that have a direct impact on marketing effectiveness is still one of the current limitations (Constantinides, 2006). Besides, Constantinides (2006) also revealed that ignoring the human factor is one of the weaknesses of the 4 Ps (marketing mix). Therefore, clarifying the determinants of satisfaction and intention to repurchase food online in this study is a significant requirement since it not only provides accurate subjects of marketing activities but also highlights insight into human factors such as satisfaction and intention.

Related to the respondents of the study, the author aims at the members of Generation Z, consisting of people born since 1995 who are into technology and consider it a part of life, are called the Digital Natives (Berkup, 2014; Levickaite, 2010). Furthermore, a generation is a group of individuals born during a specific period who have different behavioural traits and consumer behaviours (Lissitsa & Kol, 2021). Generation Z considers technology as a part of ordinary life but not an innovation, convenience or a demand one must be acclimated to (Berkup, 2014). Up to now, there has been limited research on factors affecting satisfaction and the intention to repurchase food online among Gen Z (Bhutto et al., 2023; Gunarathna & de Silva, 2022) under the crisis context.

This study examines the determinants of satisfaction and intention to repurchase food online through Grabfood application using the data collected from 652 Gen Z respondents in the south of Vietnam (from June 10, 2022, to June 25, 2022) and a conceptual framework formulated based on the theory of planned behaviour (TPB) combined with the UTAUT2 and e-SELFQUAL models to add additional factors. Four significant contributions to existing research concentrated on customer satisfaction and the intention to repurchase food online were clarified in this study. First, it provides a comprehensive model to examine the factors affecting customer satisfaction as well as repurchase intention. Second, this study demonstrated and identified the determinants of repurchase intention for buying food online under the crisis that can happen in the future. Hence, it provides for policymakers to have an insight into the context of an emerging market affected by COVID-19 (Vietnam). Third, it responds to researchers’ calls towards the factors affecting repurchase intention (Hsu & Lin, 2016; Nguyen et al., 2021; Prasetyo et al., 2021; Rita et al., 2019) as well as provides a different approach from the previous ones, including the consideration of repurchase intention constructed from TPB and combining the other stimulus factors (social influence, promotion, cost, and convenience). Finally, the managers can recognize the business implications based on the findings of this study to promote and decide the marketing strategies for online customers in a specific generation.

The rest of this paper is divided into five sections. Section 2 synthesizes the available literature to adopt the research ‘s theoretical framework and build up the research hypotheses. Section 3 presents the research methodology with the process of data collection and analysis methods. The result will be presented in Section 4, and Section 5 discusses the findings in detail. The conclusion and limitation will be placed in the final section.

2. Conceptual Framework and Hypothesis Development

2.1. Theory Development and Literature Review

The theory of planned behaviour (TPB) has been widely adopted by many authors when studying repurchase intention (Hasan, 2021; Kim & Lee, 2019). TPB emphasizes theoretical constructs representing the individual's motivational and cognitive variables as key determinants of behavioural performance (Ajzen, 1991). People's behavioural intentions, which are "an indication of how much people are willing to try and how much effort they intend to put into performing the behaviour" (Ajzen, 1991), are a fundamental component of TPB. Nevertheless, one of the criticisms of TPB theory is that subjective norms tend to be weak predictors of intention (Miles, 2012). Furthermore, TPB theory also assumes a behavioural approach in one's environment that induces intentions and behaviour, ignoring individual processes and perceptions such as personality and outcome expectations (Bandura, 2003; Miles, 2012). Nguyen Thi et al. (2022) indicated that the Theory of Planned Behaviour should be combined with other well-known models to better model the continuance intention. Hence, this is a call for researchers to find out the additional factors to develop a comprehensive model in specific circumstances.

The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) developed by (Venkatesh et al., 2012) has more supplementation and completion than previous models such as TRA (Ajzen & Fishbein, 1980), TAM (Davis, 1985), TPB (Ajzen, 1991), and UTAUT1 (Venkatesh et al., 2003), with seven factors influencing intended behaviour: Performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit. According to Tamilmani et al. (2021), UTAUT2 was indicated as a high-quality theory on most dimensions, and several studies integrated the entire UTAUT2 or part of the model with at least one other theory of theoretical significance in research models. Respond to the findings, the author developed the research model with the combination of TPB, UTAUT2 and e-SELFQUAL model (Ding et al., 2011) to examine the determinants affecting the Gen Z satisfaction and intention to repurchase food online under the crisis context (Figure 1).

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Figure 1: Proposed theoretical model

2.2. Repurchase Intention (RI)

Repurchase intention is described as an individual's decision to buy a specific service from the same firm again, taking into account his or her current position and predicted conditions (Hellier et al., 2003). Additionally, e-repurchase intention refers to a consumer's intention to repurchase from the same shop in the future (Javed & Wu, 2020). Another study defines it as the likelihood that consumers will acquire services and items from the same e-retailer on multiple occasions (Quan et al., 2020). To explain the intention to repurchase, several studies have been conducted based on the Theory of Planned Behaviour (Kazemi et al., 2013; Kim & Lee, 2019), the Technology Acceptance Model (Nguyen Thi et al., 2022), the Unified theory of acceptance and use of technology (Miao et al., 2022), the Unified theory of acceptance and use of technology 2 (Bhatti et al., 2016).

2.3. Satisfaction (SA)

Service satisfaction is the pleasure that comes from using new technology, which has been shown to play an important role in the adoption and use of technology. This factor was studied and expanded by Brown and Venkatesh (2005) in the Hedonic motivation factor. In the study by Alalwan (2020) on food delivery applications in Jordan, customers feel satisfied with the experience on the application and are willing to continue using these applications in the future if the applications bring a feeling of pleasure, comfort and enjoyment. In addition, many studies provide further evidence of the positive relationship between satisfaction and repurchase intention (Curtis et al., 2011; Fornell et al., 1996; Ilyas et al., 2020; Mittal & Kamakura, 2001). Therefore, the first hypothesis is proposed that:

H1: Satisfaction has a positive effect on repurchase intention on the Grabfood application.

2.4. Social Influence (SI)

Social influence is defined as the degree to which an individual perceives that others believe they should use a service (Venkatesh et al., 2012). According to Friedkin(2003), the term "social influence" refers to how social interactions affect a person's beliefs, attitudes, thoughts, and behaviours. Social influence can occur in the context of social commerce, where customers of Social Commerce sites communicate online to share ratings and suggestions. Research by Singh et al. (2017) has shown that social influence factors affect online purchase intention. In addition, social influence, particularly in the form of recommendations from friends and family, positively affected consumer loyalty and intentions to repurchase a service (Hossain et al., 2021). Research by Lee et al. (2019) indicated that social influence is one of the factors that positively influence the intention to continue using food delivery apps in Korea. Based on these findings, two hypotheses are proposed:

H2: Social influence has a positive effect on repurchase intention on the Grabfood application.

H3: Social influence has a positive effect on satisfaction when buying food on the Grabfood application.

2.5. Price Value (PV)

Price value (delivery service cost (CS) and purchase price promotion (PR)) is understood as the customer's perceived balance between the benefits of a service and the monetary cost of using them (Venkatesh et al., 2012). When customers perceived a higher price as an indicator of higher quality, their satisfaction and repurchase intentions were positively influenced; otherwise, if customers perceived a higher price as unjustifiably high or overpriced, it negatively affected their satisfaction and repurchase intentions (Do et al., 2023). Research by Alalwan (2020) shows that price positively affects the intention to continue using the mobile food ordering application. Furthermore, price had a significant influence on customer satisfaction and loyalty (He et al., 2008; Pandey et al., 2020). Additionally, Nguyen et al. (2021) called for papers to expand the sample and measure the effects of more independent variables on customer satisfaction and repurchase intention, such as price and promotion. Therefore, hypotheses are proposed as follows:

H4: Reasonable cost of delivery service has a positive effect on repurchase intention on the Grabfood application.

H5: Reasonable cost of delivery service has a positive effect on satisfaction when buying food on the Grabfood application.

H6: Purchase price promotion has a positive effect on repurchase intention on the Grabfood application.

H7: Purchase price promotion has a positive effect on satisfaction when buying food on the Grabfood application.

2.6. Convenience (CV)

Service Convenience (CV) refers to the time and effort that consumers used to acquire goods, rather than a product characteristic (Brown, 1990). Convenience drives retention since it saves customers time and reduces hassle (Gupta & Kim, 2007; Hsu et al., 2014). Convenience is essential in the model as it has a significant effect on satisfaction (Berry et al., 2002; Duarte et al., 2018; Ngoc Thuy, 2011). According to Colwell et al. (2008), convenience has a direct influence on customer satisfaction and is considered one of the important factors of service quality. In a study by Chang et al. (2013), well-designed online or self-service platforms may increase the effect of service convenience on return intention. Based on the above analysis, the study proposes the following hypotheses:

H8: Convenience has a positive effect on repurchase intention on the Grabfood application.

H9: Convenience has a positive effect on satisfaction when buying food on the Grabfood application.

3. Methods

3.1. Data Collection and Measurement Scales

Data were gathered in South Vietnam using an online survey methodology with a structured questionnaire and a convenience sample method from June 10, 2022 to June 25, 2022. The respondents are mostly Gen Z, aged 15 to 25. Kline (2023) recommended a sample size of 10 for one observed variable. This research, on the other hand, performed a survey with 1000 respondents, of whom 652 legitimate responses were recorded. Table 1 describes the respondents’ profile:

Table 1: Demographic characteristics

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The measurement scales were interval scales with a five-point Likert scale: Strongly disagree (1), disagree (2), neutral (3), agree (4), and strongly agree (5) in order to constitute the main content of the questionnaire. Table 2 describes the scale of research variables:

Table 2: Official measurement scales of the study

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3.2. Analysis Methods

To test the reliability of scales, Cronbach’s alpha was applied by using the SPSS. Cronbach's alpha scores should be greater than 0.7 with corrected item-to-total correlations of 0.5 and above to ensure good internal consistency and reliability (Allen et al., 2014; Hair Jr et al., 2021). The SmartPLS was used to test the measurement model following the criteria (Table 3). Then, to test the hypotheses as well as assess the structural model, the author suggests running Partial Least Squares Structural Equation Modelling (PLS-SEM) due to the corresponding of examining the complicated relationships between the multiple indirect and direct impacts (Hair Jr et al., 2021).

Table 3: The criteria for testing the measurement model

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4. Research Results

4.1. Assessment of Measurement Model

In order to assess the reliability and validity of variables, the thresholds of Cronbach’s Alpha (α) and composite reliability (CR) ≥ 0.7 (Hair et al., 2014; Hair Jr et al., 2021) were applied. Based on the results in Table 4, the minimum α and CR values were 0.727 and 0.846, respectively. Regarding the variables of the measurement scale, the total initial items were 29, and after EFA, the eliminated items were 3 (CV1, CV7, and SI6). Thus, the structural model analysis was conducted with 26 valid items.

Table 4: Factor loadings, reliability and convergent validity

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In terms of convergent validity, the threshold of outer loading and AVE values were 0.7 and 0.5, respectively. In this study, the minimum outer loading is 0.704, and all AVE values are higher than 0.5. Therefore, the convergent validity was satisfied.

Related to discriminant validity of the measurement model, the Fornell-Larcker criteria and the Heterotrait-Monotrait ratio (HTMT) were all applied. The results of testing the discriminant validity of the measurement model were presented in Table 5. All the HTMT values in this study were under 0.85 (Ringle et al., 2015). Hence, the measurement model’s discriminant validity was satisfied.

Table 5: Heterotrait-monotrait ratio (HTMT) – Matrix

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4.2. Assessment of Structural Model

Hair et al. (2019) suggest that the problem of collinearity should be considered when evaluating structural models. As a result, all VIF values in this study were less than 3, showing that collinearity was not a concern.

To test the research hypothesis and the effect coefficient, the bootstrapping procedure according to the proposed repeated sample size is 5000 (Henseler et al., 2016). The proposed hypotheses are evaluated for statistical significance at the 99%, 95%, and 90% confidence levels for p-values compared with 1%, 5%, and 10%, respectively.

Table 6: Hypothesized structural paths (bootstrapping 5000)

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In terms of the results of testing the hypotheses, all path coefficients were found to have significant levels of 1%. Hence, all hypotheses were accepted, and the positive impacts of satisfaction on repurchase intention (β = 0.284), social influence on repurchase intention (β = 0.251), social influence on satisfaction (β = 0.151), cost on repurchase intention (β = 0.128), cost on satisfaction (β = 0.255), promotion on repurchase intention (β = 0.151), promotion on satisfaction (β = 0.275), convenience on repurchase intention (β = 0.128), and convenience on satisfaction (β = 0.345), were confirmed.

Related to the model fit, Henseler et al. (2016) indicated the level of explanation for the dependent variable R2, including: R2 > 0,75 (strong prediction); 0,75 ≥ R2 > 0,5 (average prediction); 0.5 ≥ R2 > 0,25 (weak prediction). In this study, the explanatory power or prediction of repurchase intention was average (R2 = 0.6). Similar to repurchase intention, the explanatory power of satisfaction was average (R2 = 0.676). Based on these results (Table 7), it can be concluded that the model has an acceptable fit as well as an acceptable level of data explanation.

Table 7: Results of R-square

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Regarding mediating effects, the mediating role of satisfaction was confirmed. The results revealed that satisfaction mediated the relationship between convenience, social influence, promotion, cost and repurchase intention (Table 8). Specifically, the p-values of CV→SA→RI, SI→SA→RI, PR→SA→RI, and CS→SA→RI were less than 0.01 (1%).

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Figure 2: Presenting the final path model

Table 8. The results of the mediating effects of satisfaction

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5. Discussion

By combining the UTAUT2, e-SELFQUAL models (Ding et al., 2011), and brief TPB to examine the determinants affecting Gen Z satisfaction and intention to repurchase food online under the context of a crisis, the results of this study indicated that the research model well explained the repurchase intention (R2 = 0.6) in the context of a crisis (e.g., COVID-19) as well as having higher explanatory power than previous studies (Nguyen Thi et al., 2022; Tri Cuong, 2021). Furthermore, this study demonstrated and identified the determinants of repurchase intention for buying food online under the crisis that can happen in the future.

In terms of the related models, Tri Cuong (2021) built up the model to explain the rebuying intention in online shopping, in which trust and convenience affected directly and indirectly via satisfaction the rebuying intention. In another study, Nguyen Thi et al. (2022) applied the TAM model to explain the repurchase intention and demonstrate the mediating role of satisfaction in forming the repurchase intention. Compare to above studies of Tri Cuong (2021) and Nguyen Thi et al. (2022), this study confirmed one again the critical mediating role of satisfaction in shaping the repurchase intention. On the other hand, this study also provided a different approach from the previous ones, including the consideration of repurchase intention constructed from TPB and combining the other stimulus factors (social influence, promotion, cost, and convenience).

Another highlight in this study is the combination of factors such as social influence and satisfaction in building a research model. Social influence is described as an adjustment in a person's thoughts, emotions, attitudes, or behaviours as a result of interaction with a person or a group (Rashotte, 2007). Social influence mentioned variables which conclude group norms, and social identity (Bagozzi & Dholakia, 2002). In addition, satisfaction is considered a cognitive and emotional factor that is influenced by environmental factors before forming repurchase intention. According to Cengiz (2010), customer satisfaction is defined as the degree to which a customer believes that an individual, corporation, or organisation has effectively supplied a product or service that satisfies the demands of the customer in the situation in which the consumer is aware of and/or utilising the product or service. Hence, the inclusion of social influence and satisfaction variables in this research model to explain repurchase intention is a significant theoretical contribution in perfecting the brief TPB model in a specific context.

On the other hand, despite the fact that this is only a case study in an emerging economy (Vietnam), it is an actual instance of identifying the determinants of the intentions of buyers and service users in crisis circumstances. This research was conducted in the context of the crisis (COVID-19) to provide an overall picture of the decisive factors affecting customer satisfaction and repurchase intention. The findings will therefore provide policymakers with insight into the intentions and behaviours of consumers purchasing goods or utilising services in cases of crisis that may arise in the future due to a shortage of fundamental requirements (such as food, vital consumer products, and so on).

Based on the direct effects of the study, managers can enhance not only customer satisfaction but also the repurchase intention related to buying food online. The positive impact of convenience on satisfaction (β = 0.345) and repurchase intention (β = 0.128) indicates that all efforts and activities to enhance convenience related to sales and customer care services help increase customer satisfaction and repurchase intention. Similar to convenience, the positive effect of price value (delivery service cost and purchase price promotion) on customer satisfaction and repurchase intention reveals that all efforts and activities to increase customer awareness between the benefits of services and the monetary cost of using them will enhance the customer satisfaction and repurchase intention. Besides, the positive influence of social influence on customer satisfaction and repurchase intention expresses that customer satisfaction and repurchase intention will be increased by improving marketing activities connected to purchasing groups and using the social aspects of communication such as social media, e-commerce platforms.

According to these results, several implications for management, government were proposed in the context of crises, as follows:

⦁ Planning for crisis situations by building backup distribution channels to give people the most convenient access to products and services.

⦁ The government needs to combine with charitable organizations to establish support funds for users/customers at low prices or adjust promotion policies to reach the majority of users/customers.

⦁ Enterprises need to closely follow government regulations and flexibly build production, distribution, and marketing plans to minimize costs and increase product coverage for the majority of users.

⦁ Building marketing channels through social networking platforms and electronic word of mouth (eWOM) in order to strengthen community influence on products/services as well as safe and economical delivery methods.

In addition, in the context of a crisis (such as COVID-19), almost all users/customers are at home, so those who are good at using smart devices and online applications, Gen Z, are the majority of the population. Hence, in terms of these customers, managers must be extremely attentive as well as clearly target their marketing messages.

⦁ Combining both traditional and modern approaches to increase user acceptance of state and business support policies.

⦁ To better orient Gen Z, promote media coverage of government policies and product and service information.

⦁ Promote volunteer campaigns aimed at Gen Z users/customers to support and propagate the benefits of complying with disease prevention regulations and accessing products/services.

6. Limitations and Future Research

Despite the fact that the study has significant theoretical and practical implications, some limitations should be considered for future research. First, the convenience sampling method would bring about biases; therefore, the future research can conduct research longitudinally and across multiple crisis periods. Second, although the study’s focus on a specific context (COVID-19 in Vietnam) is a particular highlight, it may not generalize to other regions or populations; hence, future research should focus on the context to generalize to other regions or populations. Third, due to the development of e-commerce and the complex behaviour of shopping online, it may be affected by a wide range of factors, some of which may not be disregarded in this study. Thus, future research should consider contextual factors such as marketing exposure and the emotional state of the customer after receiving this information.

References

  1. Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
  2. Ajzen, I., & Fishbein, M. (1980). Theory of Reasoned Action (TRA). Information Seeking Behavior and Technology Adoption.
  3. Alalwan, A. A. (2020). Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. International Journal of Information Management, 50, 28-44. https://doi.org/10.1016/j.ijinfomgt.2019.04.008
  4. Allen, P., Bennett, K., & Heritage, B. (2014). SPSS statistics version 22: A practical guide. Cengage Learning Australia. https://cengage.com.au/product/title/spss-statistics-version-22-a-practical-guide/isbn/9780170348973
  5. Alvarez-Risco, A., Del-Aguila-Arcentales, S., Rosen, M. A., & Yanez, J. A. (2022). Social Cognitive Theory to Assess the Intention to participate in the Facebook Metaverse by citizens in Peru during the COVID-19 pandemic. Journal of Open Innovation: Technology, Market, and Complexity, 8(3), 142. https://doi.org/10.3390/joitmc8030142
  6. Alyoubi, A. A. (2015). E-commerce in developing countries and how to develop them during the introduction of modern systems. Procedia Computer Science, 65, 479-483. https://doi.org/10.1016/j.procs.2015.09.127
  7. Bagozzi, R. P., & Dholakia, U. M. (2002). Intentional social action in virtual communities. Journal of interactive marketing, 16(2), 2-21. https://doi.org/10.1002/dir.10006
  8. Bandura, A. (2003). Social cognitive theory for personal and social change by enabling media. In Entertainment-education and social change (pp. 97-118). Routledge.
  9. Berkup, S. B. (2014). Working with generations X and Y in generation Z period: Management of different generations in business life. Mediterranean journal of social Sciences, 5(19), 218. https://doi.org/10.5901/mjss.2014.v5n19p218
  10. Berry, L. L., Seiders, K., & Grewal, D. (2002). Understanding service convenience. Journal of marketing, 66(3), 1-17. https://doi.org/10.1509/jmkg.66.3.1.18505
  11. Bharadwaj, P. N., & Soni, R. G. (2007). E-commerce usage and perception of e-commerce issues among small firms: results and implications from an empirical study. Journal of small business management, 45(4), 501-521. https://doi.org/10.1111/j.1540-627X.2007.00225.x
  12. Bhatti, H., Abareshi, A., & Pittayachawan, S. (2016). An evaluation of customer repurchase behaviour in mobile telecommunication services in Australia. 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM),
  13. Bhutto, M. Y., Ertz, M., Soomro, Y. A., Khan, M. A. A., & Ali, W. (2023). Adoption of halal cosmetics: extending the theory of planned behavior with moderating role of halal literacy (evidence from Pakistan). Journal of Islamic Marketing, 14(6), 1488-1505. https://doi.org/10.1108/JIMA-09-2021-0295
  14. Brown, L. G. (1990). Convenience in services marketing. Journal of Services Marketing, 4(1), 53-59. https://doi.org/10.1108/EUM0000000002505
  15. Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS quarterly, 399-426. https://doi.org/10.2307/25148690
  16. Burt, S., & Sparks, L. (2003). E-commerce and the retail process: a review. Journal of Retailing and Consumer services, 10(5), 275-286. https://doi.org/10.1016/S0969-6989(02)00062-0
  17. Cengiz, E. (2010). Measuring customer satisfaction: must or not. Journal of naval science and engineering, 6(2), 76-88.
  18. Chang, M.-Y., Chen, K., Pang, C., Chen, C.-M., & Yen, D. C. (2013). A study on the effects of service convenience and service quality on maintenance revisit intentions. Computer Standards & Interfaces, 35(2), 187-194. https://doi.org/10.1016/j.csi.2012.08.002
  19. Colwell, S. R., Aung, M., Kanetkar, V., & Holden, A. L. (2008). Toward a measure of service convenience: multiple-item scale development and empirical test. Journal of Services Marketing, 22(2), 160-169. https://doi.org/10.1108/08876040810862895
  20. Constantinides, E. (2006). The marketing mix revisited: towards the 21st century marketing. Journal of marketing management, 22(3-4), 407-438. https://doi.org/10.1362/026725706776861190
  21. Curtis, T., Abratt, R., Rhoades, D., & Dion, P. (2011). Customer loyalty, repurchase and satisfaction: A meta-analytical review. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 24, 1-26.
  22. Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results Massachusetts Institute of Technology]. http://hdl.handle.net/1721.1/15192
  23. Ding, D. X., Hu, P. J.-H., & Sheng, O. R. L. (2011). e-SELFQUAL: A scale for measuring online self-service quality. Journal of Business Research, 64(5), 508-515. https://doi.org/10.1016/j.jbusres.2010.04.007
  24. Do, Q. H., Kim, T. Y., & Wang, X. (2023). Effects of logistics service quality and price fairness on customer repurchase intention: The moderating role of cross-border e-commerce experiences. Journal of Retailing and Consumer services, 70, 103165. https://doi.org/10.1016/j.jretconser.2022.103165
  25. Duarte, P., e Silva, S. C., & Ferreira, M. B. (2018). How convenient is it? Delivering online shopping convenience to enhance customer satisfaction and encourage e-WOM. Journal of Retailing and Consumer services, 44, 161-169. https://doi.org/10.1016/j.jretconser.2018.06.007
  26. Fornell, C., Johnson, M. D., Anderson, E. W., Cha, J., & Bryant, B. E. (1996). The American customer satisfaction index: nature, purpose, and findings. Journal of marketing, 60(4), 7-18. https://doi.org/10.1177/002224299606000403
  27. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
  28. Friedkin, N. E. (2003). Social influence network theory: Toward a science of strategic modification of interpersonal influence systems. na.
  29. Gotz, O., Liehr-Gobbers, K., & Krafft, M. (2009). Evaluation of structural equation models using the partial least squares (PLS) approach. In Handbook of partial least squares: Concepts, methods and applications (pp. 691-711). Springer.
  30. Gunarathna, P., & de Silva, L. (2022). Factors Influencing Online Fast-Food Ordering Intension among Millennials and Generation Z-Evidence from Sri Lanka. 2022 Moratuwa Engineering Research Conference (MERCon),
  31. Gupta, S., & Kim, H.-W. (2007). The moderating effect of transaction experience on the decision calculus in on-line repurchase. International journal of electronic commerce, 12(1), 127-158. https://doi.org/10.2753/JEC1086-4415120105
  32. Hair, J. F., Gabriel, M., & Patel, V. (2014). AMOS covariance-based structural equation modeling (CB-SEM): Guidelines on its application as a marketing research tool. Brazilian Journal of Marketing, 13(2). https://doi.org/10.5585/remark.v13i2.2718
  33. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European business review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203
  34. Hair Jr, J., Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
  35. Hasan, S. (2021). Assessment of electric vehicle repurchase intention: A survey-based study on the Norwegian EV market. Transportation Research Interdisciplinary Perspectives, 11, 100439. https://doi.org/10.1016/j.trip.2021.100439
  36. He, Y., Chan, L. K., & Tse, S.-K. (2008). From consumer satisfaction to repurchase intention: The role of price tolerance in a competitive service market. Total Quality Management, 19(9), 949-961. https://doi.org/10.1080/14783360802224628
  37. Hellier, P. K., Geursen, G. M., Carr, R. A., & Rickard, J. A. (2003). Customer repurchase intention: A general structural equation model. European journal of marketing, 37(11/12), 1762-1800. https://doi.org/10.1108/03090560310495456
  38. Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial management & data systems, 116(1), 2-20. https://doi.org/10.1108/IMDS-09-2015-0382
  39. Hoang, T. P. T., & Lam, Q. L. (2021). The factors affect consumer's trust and continuous usage intention of food delivery mobile apps. Journal of Science - Economics and Business Administration. https://doi.org/10.46223/HCMCOUJS.econ.vi.16.2.931.2021
  40. Hossain, M. A., Yesmin, M. N., Jahan, N., & Kim, M. (2021). Effects of service justice, quality, social influence and corporate image on service satisfaction and customer loyalty: moderating effect of bank ownership. Sustainability, 13(13), 7404. https://doi.org/10.3390/su13137404
  41. Hsu, C.-L., & Lin, J. C.-C. (2016). Effect of perceived value and social influences on mobile app stickiness and in-app purchase intention. Technological forecasting and social change, 108, 42-53. https://doi.org/10.1016/j.techfore.2016.04.012
  42. Hsu, M.-H., Chang, C.-M., Chu, K.-K., & Lee, Y.-J. (2014). Determinants of repurchase intention in online group-buying: The perspectives of DeLone & McLean IS success model and trust. Computers in Human Behavior, 36, 234-245. https://doi.org/10.1016/j.chb.2014.03.065
  43. Huang, L., Huang, Y., Huang, R., Xie, G., & Cai, W. (2022). Factors influencing returning migrants' entrepreneurship intentions for rural E-commerce: an empirical investigation in China. Sustainability, 14(6), 3682. https://doi.org/10.3390/su14063682
  44. Ilyas, G. B., Rahmi, S., Tamsah, H., Munir, A. R., & Putra, A. H. P. K. (2020). Reflective model of brand awareness on repurchase intention and customer satisfaction. The Journal of Asian Finance, Economics and Business, 7(9), 427-438. https://doi.org/10.13106/jafeb.2020.vol7.no9.427
  45. Javed, M. K., & Wu, M. (2020). Effects of online retailer after delivery services on repurchase intention: An empirical analysis of customers' past experience and future confidence with the retailer. Journal of Retailing and Consumer services, 54, 101942. https://doi.org/10.1016/j.jretconser.2019.101942
  46. Kazemi, A., Abadi, H. R. D., & Kabiry, N. (2013). Analyzing the effect of customer equity on repurchase intentions. International Journal of Academic Research in Business and Social Sciences, 3(6), 78.
  47. Kim, J. H., & Lee, H. C. (2019). Understanding the repurchase intention of premium economy passengers using an extended theory of planned behavior. Sustainability, 11(11), 3213. https://doi.org/10.3390/su11113213
  48. Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford publications.
  49. Kshetri, N. (2007). Barriers to e-commerce and competitive business models in developing countries: A case study. Electronic commerce research and applications, 6(4), 443-452. https://doi.org/10.1016/j.elerap.2007.02.004
  50. Kumar, A., & Kashyap, A. K. (2022). Understanding the Factors Influencing Repurchase Intention in Online Shopping: A Meta-analytic Review. Vision, 09722629221107957. https://doi.org/10.1177/09722629221107957
  51. Laudon, K. C., & Traver, C. G. (2021). E-Commerce 2021-2022: Business, Technology and Society, Global Edition. Pearson Education. https://books.google.com.vn/books?id=r9g2EAAAQBAJ
  52. Lee, S. W., Sung, H. J., & Jeon, H. M. (2019). Determinants of continuous intention on food delivery apps: extending UTAUT2 with information quality. Sustainability, 11(11), 3141. https://doi.org/10.3390/su11113141
  53. Levickaite, R. (2010). Generations X, Y, Z: How social networks form the concept of the world without borders (the case of Lithuania). LIMES: Cultural Regionalistics, 3(2), 170-183. https://doi.org/10.3846/limes.2010.17
  54. Lissitsa, S., & Kol, O. (2021). Four generational cohorts and hedonic m-shopping: association between personality traits and purchase intention. Electronic Commerce Research, 21, 545-570. https://doi.org/10.1007/s10660-019-09381-4
  55. Miao, M., Jalees, T., Zaman, S. I., Khan, S., Hanif, N.-u.-A., & Javed, M. K. (2022). The influence of e-customer satisfaction, e-trust and perceived value on consumer's repurchase intention in B2C e-commerce segment. Asia Pacific Journal of Marketing and Logistics, 34(10), 2184-2206. https://doi.org/10.1108/APJML-03-2021-0221
  56. Miles, J. A. (2012). Management and organization theory: A Jossey-Bass reader (Vol. 9). John Wiley & Sons.
  57. Mittal, V., & Kamakura, W. A. (2001). Satisfaction, repurchase intent, and repurchase behavior: Investigating the moderating effect of customer characteristics. Journal of marketing research, 38(1), 131-142. https://doi.org/10.1509/jmkr.38.1.131.18832
  58. Nawangsari, S., Clara, D. A., & Wibowo, E. P. (2020). The Effect of Electronic Service Quality, Product, Price, Promotion on Customer Satisfaction (Case Study on Customer Online Stores Lazada. Co. Id Jakarta). APTIKOM Journal on Computer Science and Information Technologies, 5(1), 141-148. https://doi.org/10.34306/csit.v5i1.115
  59. Ngoc Thuy, P. (2011). Using service convenience to reduce perceived cost. Marketing Intelligence & Planning, 29(5), 473-487. https://doi.org/10.1108/02634501111153683
  60. Nguyen, L., Nguyen, T. H., & Tan, T. K. P. (2021). An empirical study of Customers' satisfaction and repurchase intention on online shopping in Vietnam. The Journal of Asian Finance, Economics and Business, 8(1), 971-983. https://doi.org/doi:10.13106/jafeb.2021.vol8.no1.971
  61. Nguyen Thi, B., Tran, T. L. A., Tran, T. T. H., Le, T. T., Tran, P. N. H., & Nguyen, M. H. (2022). Factors influencing continuance intention of online shopping of generation Y and Z during the new normal in Vietnam. Cogent Business & Management, 9(1), 2143016. https://doi.org/10.1080/23311975.2022.2143016
  62. Pandey, N., Tripathi, A., Jain, D., & Roy, S. (2020). Does price tolerance depend upon the type of product in e-retailing? Role of customer satisfaction, trust, loyalty, and perceived value. Journal of Strategic Marketing, 28(6), 522-541. https://doi.org/10.1080/0965254X.2019.1569109
  63. Prasetyo, Y. T., Tanto, H., Mariyanto, M., Hanjaya, C., Young, M. N., Persada, S. F., Miraja, B. A., & Redi, A. A. N. P. (2021). Factors affecting customer satisfaction and loyalty in online food delivery service during the COVID-19 pandemic: Its relation with open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 76. https://doi.org/10.3390/joitmc7010076
  64. Quan, N., Chi, N., Nhung, D., Ngan, N., & Phong, L. (2020). The influence of website brand equity, e-brand experience on eloyalty: The mediating role of e-satisfaction. Management Science Letters, 10(1), 63-76. https://doi.org/10.5267/j.msl.2019.8.015
  65. Qureshi, I., Fang, Y., Ramsey, E., McCole, P., Ibbotson, P., & Compeau, D. (2009). Understanding online customer repurchasing intention and the mediating role of trust-an empirical investigation in two developed countries. European Journal of Information Systems, 18, 205-222. https://doi.org/10.1057/ejis.2009.15
  66. Rashotte, L. (2007). Social influence. The Blackwell encyclopedia of sociology. https://doi.org/10.1002/9781405165518.wbeoss154
  67. Ringle, C., Da Silva, D., & Bido, D. (2015). Structural equation modeling with the SmartPLS. Bido, D., da Silva, D., & Ringle, C.(2014). Structural Equation Modeling with the Smartpls. Brazilian Journal Of Marketing, 13(2).
  68. Rita, P., Oliveira, T., & Farisa, A. (2019). The impact of e-service quality and customer satisfaction on customer behavior in online shopping. Heliyon, 5(10). https://doi.org/10.1016/j.heliyon.2019.e02690
  69. Scott, D. M. (2009). The new rules of marketing and PR: how to use social media, blogs, news releases, online video, and viral marketing to reach buyers directly. John Wiley & Sons.
  70. Singh, A., Alryalat, M. A. A., Alzubi, J. A., & Sarma, H. (2017). Understanding jordanian consumers' online purchase intentions: Integrating trust to the UTAUT2 framework. International Journal of Applied Engineering Research, 12(20), 10258-10268.
  71. Singh, J., Steele, K., & Singh, L. (2021). Combining the best of online and face-to-face learning: Hybrid and blended learning approach for COVID-19, post vaccine, & post-pandemic world. Journal of Educational Technology Systems, 50(2), 140-171. https://doi.org/10.1177/00472395211047865
  72. Tamilmani, K., Rana, N. P., Wamba, S. F., & Dwivedi, R. (2021). The extended Unified Theory of Acceptance and Use of Technology (UTAUT2): A systematic literature review and theory evaluation. International Journal of Information Management, 57, 102269. https://doi.org/10.1016/j.ijinfomgt.2020.102269
  73. Tri Cuong, D. (2021). The impact of Convenience and Trust on Satisfaction and Rebuying Intention in Online Shopping: Using The PLS Algorithm. 2021 3rd International Conference on Management Science and Industrial Engineering,
  74. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478. https://doi.org/10.2307/30036540
  75. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178. https://doi.org/10.2307/41410412
  76. Wire, B. (2020). Global Ship Repairing Market Report 2020: COVID-19 Impacts, Growth and Changes-Forecast to 2030. In.
  77. Zaremohzzabieh, Z., Ahrari, S., Krauss, S. E., Samah, A. A., Meng, L. K., & Ariffin, Z. (2019). Predicting social entrepreneurial intention: A meta-analytic path analysis based on the theory of planned behavior. Journal of Business Research, 96, 264-276. https://doi.org/10.1016/j.jbusres.2018.11.030