Factors influencing continuance intention of online shopping of generation Y and Z during the new normal in Vietnam

Thi, Binh Nguyen et al. Article Factors influencing continuance intention of online shopping of generation Y and Z during the new normal in Vietnam Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Thi, Binh Nguyen et al. (2022) : Factors influencing continuance intention of online shopping of generation Y and Z during the new normal in Vietnam, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 9, Iss. 1, pp. 1-25, https://doi.org/10.1080/23311975.2022.2143016 This Version is available at: https://hdl.handle.net/10419/289336 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oabm20 Cogent Business & Management ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/oabm20 Factors influencing continuance intention of online shopping of generation Y and Z during the new normal in Vietnam Binh Nguyen Thi, Thi Lan Anh Tran, Thi Thu Hien Tran, Thanh Thao Le, Phan Nhat Hang Tran & Minh Hieu Nguyen To cite this article: Binh Nguyen Thi, Thi Lan Anh Tran, Thi Thu Hien Tran, Thanh Thao Le, Phan Nhat Hang Tran & Minh Hieu Nguyen (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, DOI: 10.1080/23311975.2022.2143016 To link to this article: https://doi.org/10.1080/23311975.2022.2143016 © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 11 Nov 2022. Submit your article to this journal Article views: 10053 View related articles View Crossmark data Citing articles: 10 View citing articles MARKETING | RESEARCH ARTICLE Factors influencing continuance intention of online shopping of generation Y and Z during the new normal in Vietnam Binh Nguyen Thi 1 , Thi Lan Anh Tran 2 , Thi Thu Hien Tran 2,3 , Thanh Thao Le 2 , Phan Nhat Hang Tran 2 and Minh Hieu Nguyen 3 * Abstract: This study investigated the determinants of online shopping continuance intention of Generation Y and Z during the new normal. A conceptual framework, which was an extension of the Technology Acceptance Model, was empirically tested using partial least squares structural equation modelling, multi-group analysis technique, and the data collected from 847 Gen Y-ers and Gen Z-ers in Hanoi, Vietnam during March 2022. The results revealed that facilitators of repurchase intention included perceived usefulness, perceived ease of use, satisfaction, and environmental awareness while perceived risks of online shopping served as a barrier. Notably, the barrier was found to affect Gen Y’s repurchase intention more severely. Personalization was not directly associated with the intention but had strong indirect effects through perceived usefulness, perceived ease of use, and satisfaction. The risk of COVID-19 was not a predictor of online repurchase intention. Understanding of the continuance intention of online shopping among consumers from different generations in an emerging country during the new normal may aid to enhance the quality of decision-making. Specifically, platforms and sellers should adopt customized marketing programs towards Gen Y and Gen Z. Additionally, a user-friendly and informative purchasing process with personalized features should be formulated. Demonstrating online shopping as a green behavior would be useful. This study differs from earlier research by considering and comparing factors influencing the intention to keep shopping online of Gen Y and Gen Z in a developing country when the COVID-19 is well controlled. Subjects: Psychological Science; Multidisciplinary Psychology; Consumer Psychology Keywords: COVID-19; Generation; Personalization; Hanoi; TAM; Online shopping; PLS-SEM 1. Introduction Online shopping refers to the act of purchasing goods or services through the internet. In the mid1990s, the increasing popularity of the World Wide Web and the development of information and communications technologies led to the considerable emergence of online shopping (Andreev et al., 2010). According to Statista (2022a), retail e-commerce sales worldwide increased from 1,336 billion US dollars in 2014 to 4,938 billion US dollars in 2021. Online shopping has become popular in the developed countries, whereas the developing countries, particularly nations in the Southeast Asia, have experienced a lower prevalence of shopping online despite its great potential of growth (Alyoubi, 2015; Kshetri, 2007). The limited scales of e-commerce in emerging countries result from various obstacle types, including economic barriers (e.g., poor information technology Nguyen Thi et al., Cogent Business & Management (2022), 9: 2143016 https://doi.org/10.1080/23311975.2022.2143016 Page 1 of 25 Received: 04 July 2022 Accepted: 30 October 2022 *Corresponding author: Minh Hieu Nguyen, Faculty of TransportEconomics, University of Transport and Communications, Hanoi, Vietnam E-mail: [email protected] Reviewing editor: Len Tiu Wright, De Montfort University Faculty of Business and Law, United Kingdom Additional information is available at the end of the article © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. infrastructure, low rate of payment by credit card or e-wallet), socio-political barriers (e.g., lack of government regulations, high taxes), and cognitive barriers (e.g., uncertain awareness of the benefits and consequences, changes of consumers’ choice architecture; (Alyoubi, 2015; Babenko et al., 2019; Lawrence & Tar, 2010). The rapid outbreak of the COVID-19 pandemic has upended life and accelerated online shopping further since online shopping reduces physical interactions, thus preventing the coronavirus’s dissemination (Al-Hattami, 2021; Koch et al., 2020; Nguyen et al., 2021a). A remarkable shift from going to brick-and-mortar stores to one-click shopping has been recorded with an average growth of 10% in the online customer base of 45 countries across the world (McKinsey & Company, 2020). A report by Verdon (2021) shows that the purchase volume of e-commerce for the first quarter of 2021 increased by 38%, compared to that of 2020. The presence of COVID-19 is a tremendous shock, marking a turning point in the growth of e-commerce in developing countries (UNCTAD, 2021). Taking Thailand as an example, its e-commerce grew 19% in 2020 with an expected steady compound annual growth rate of 7.4% to 2024, pushing shopping online as a persistent habit (J.P. Morgan, 2021). So far, the large percentages of adults (over threequarters) in many emerging countries are fully vaccinated, enabling citizens’ daily lives to return to their pre-COVID-19 status (Ritchie et al., 2020). Currently, purchasing in online channels, despite being at a larger scale than that before the COVID-19ʹs occurrence, is falling owing to the lessened infection fear and more access to physical stores (Han et al., 2022). In contrast to the ample knowledge on the determinants of virtual shopping during the COVID-19, understanding the factors influencing the intention to continue shopping online when inhabitants are familiar with and ready to live with the coronavirus, to the best of our knowledge, is limited. A focus of research on online shopping behaviors is on intergenerational comparison, whose findings would be informative for formulating the strategy of the segment market (Lissitsa & Kol, 2016). A generation includes a cohort of persons who are born within a particular period and have distinct personality traits and consumer behaviors (Lissitsa & Kol, 2021). Persons born from 1981 to 1996 are known as Generation Y (Gen Y); meanwhile, members of Generation Z (Gen Z) were born between 1997 and 2012 (Dimock, 2019). Both generations are technological and media savvy and have a high rate of exposure to the Internet (compared to the older cohorts); however, their differences in experiences, attitudes, and preferences for online shopping significantly influence their choice of shopping types (Dabija & Lung, 2019). Before COVID 19, Gen Y was perceived as consumption-driven with greater purchasing power while Gen Z was associated with “online store disloyalty” and pragmatism in purchasing due to their extensive value comparison among e-stores (Lissitsa & Kol, 2016). Notably, Koch et al. (2020) found that Gen Z consumers reveal higher hedonic motivations in e-shopping than Gen Y consumers, highlighting the certain discrepancies between these generations in the health crisis era. Notwithstanding, little, so far, is known about the factors associated with continuance intention to shop online of Gen Y and Gen Z. This study investigates the determinants of the continuance intention to shop online during the new normal using the data collected from 847 Gen Y and Gen Z respondents in Hanoi, Vietnam during March 2022 and a conceptual framework formulated based on the Technology Acceptance Model (TAM). The specific research questions are as follows: Q1: What are the driving factors of continuance intention of online shopping? Q2: What are the barriers to continuance intention of online shopping? Q3: What are the differences in the effects of factors on continuance intention of online shopping among generational cohorts (i.e., Y and Z)? This study has made both theoretical and practical contributions. First, it provides novel insights into facilitators and deterrents to the continuance intention of online shopping in the new normal. Nguyen Thi et al., Cogent Business & Management (2022), 9: 2143016 https://doi.org/10.1080/23311975.2022.2143016 Page 2 of 25 Second, the research reports large positively significant mediating effects of personalization on repurchase intention. Third, the impact of the perceived risk of COVID-19 on the continuance intention is found to be insignificant, demonstrating online shopping as a consistent action in the new normal rather than a response to the infection danger. Fourth, the perceptions of online shopping risks have a greater negative effect on the continuance intention of Gen Y in comparison to Gen Z. The fifth contribution is business implications proposed based on the findings of influential factors for marketing managers and e-commerce platforms to promote the loyalty of online shopping users from different generational cohorts. The remainder of this paper is divided into five sections. Section 2 synthesizes the existing literature to adopt the theoretical framework of this research. Subsequently, Section 3 describes the process of data collection and methods used to analyze the data. In the fourth section, the results are presented before Section 5 discusses and interprets the findings in detail. Conclusions and future research directions are drawn in the final section. 2. Conceptual framework foundation 2.1. Repurchase intention (RI) Repurchase intention is defined as an individual’s judgment regarding purchasing a specific service again from the same firm, considering his or her existing condition and expected circumstances (Hellier et al., 2003). In the online shopping context, repurchase intention refers to the subjective likelihood that customers will continue to purchase products from an online seller or use a previous online channel to shop at a certain vendor (Khalifa & Liu, 2007). Acquiring new customers usually takes much more time, expense, and effort than retaining existing ones; therefore, promoting the repurchase intention of customers could help maintain and improve any company’s profitability (Spreng et al., 1995, Nguyen et al., 2022). In addition, for enterprises, customer retention is a means of creating competitive advantages and achieving sustainable development goals (Tsai & Huang, 2007). It is, hence, essential to investigate the determinants of customer repurchase intention. 2.2. Technology Acceptance Model (TAM) TAM, developed by Davis (1985), explains an individual’s acceptance of a particular innovative technology through four fundamental predictors: Perceived Usefulness (PU), Perceived Ease of Use (PEU), Attitude (ATT), and Behavioral Intention (Figure 1). As a result of appreciating TAM as a robust theoretical foundation to parsimoniously link beliefs to behavior, scholars have deployed TAM to analyze (the potential of) technological adoptions in a variety of contexts (Hubert et al., 2017). Because online shopping behaviors use the internet as the technological environment for making purchase decisions, TAM has been intensively utilized to predict the repurchase intention of consumers (Trivedi & Yadav, 2018; Wen et al., 2011). Among three antecedents of the behavioral intention (see, Figure 1), ATT has frequently been claimed to be eliminated from the original model (Brown et al., 2002; Gefen et al., 2003; S. Wang et al., 2016). This results from the fact that ATT does not take a significant role in the overall variance in technological usage (Teo & Noyes, 2011; Zacharis, 2012). Besides, behavioral intention Figure 1. The classic TAM. Nguyen Thi et al., Cogent Business & Management (2022), 9: 2143016 https://doi.org/10.1080/23311975.2022.2143016 Page 3 of 25 is demonstrated to be an effective predictor of actual use (Davis, 1989; Venkatesh et al., 2003) and directly affected by perceived usefulness and perceived ease of use (Wu & Wang, 2005). Having considered the better explanatory power of the model, this paper adopts a parsimonious TAM version with a removal of ATT. According to Davis (1989), PU is defined as the extent to which use of the technology will increase an individual’s productivity. The positive relationship between behavioral intention and PU is widely demonstrated (Venkatesh & Davis, 2000). In online purchasing, PU refers to the extent to which consumers believe that using the Internet will improve their performance, thereby enhancing the shopping experience (Perea et al., 2004). As reported by Chiu et al. (2009), the consumers’ repurchase intention will be higher if they are fully aware of the usefulness of the experience. PEU is known as the degree to which the use of the technology is supposed to be effortless and easy (Venkatesh & Davis, 2000). PEU is a significant predictor of behavioral intention (Abdullah et al., 2016; Amin et al., 2014b). Hamid et al. (2016) indicate that when a consumer believes it is easy to purchase a product, his/her intention to repurchase it will increase. PEU is found to have a positive effect on PU as the easier a technology is to use, the more useful it can be (Davis, 1989; Venkatesh & Davis, 2000). Based on the abovementioned discussions, the following hypotheses are proposed: H1: Perceived usefulness positively influences the repurchase intention. H2a: Perceived ease of use positively influences the repurchase intention. H2b: Perceived ease of use positively influences perceived usefulness. TAM is typically a technology-oriented model whose fundamental constructs do not comprehensively drive the variety of user task environments; thus, it needs to be extended to better explain the adoption behavior of innovative technology (Lu et al., 2005; Schepers & Wetzels, 2007). This research enhances the TAM model by adding five constructs: personalization, perceived risks of online shopping, perceived risk of COVID-19, environmental awareness, and satisfaction (Figure 2). 2.3. Perceived risks of online shopping (PR_OS) Cunningham (1967) suggests that risk comprises two dimensions: uncertainty and consequences. Peter and Ryan (1976) in the effort to understand consumer’s behavior categorize the risk into the probability of consequences occurring and adverse effects of poor consumer’s choice. Facing risks is a part of using a (new) technology and perceived risk is consistently found to be an impediment to behavioral intention (G.S. Kim et al., 2008; Luo et al., 2010). Unfortunately, TAM lacks constructs related to risks and loss (Lu et al., 2005; Natarajan et al., 2018). It is, therefore, necessary to integrate the consumers’ perceived risks into TAM (Shukla et al., 2021). However, risks may vary across industries or sectors. When purchasing online, customers may confront various types (e.g., financial, product, time, delivery, privacy) of risks. Perceived risks of online shopping is defined as the likelihood of experiencing loss when buying goods in online stores (Ko et al., 2004). This study focuses on the two most common kinds—financial risk and product risk. The former is the possibility of losing money, or the consumer’s sense of insecurity when utilizing payment online (Masoud, 2013). The latter is the perception that a purchased product may not be as expected (Masoud, 2013). A large number of researchers have agreed that perceived risks of online shopping have adverse effects on the adoption of purchasing virtually (Kok Wai, Dastane et al., 2019; Lingying Zhang et al., 2012) and perceived usefulness in online shopping and online transactions Nguyen Thi et al., Cogent Business & Management (2022), 9: 2143016 https://doi.org/10.1080/23311975.2022.2143016 Page 4 of 25 (Featherman & Pavlou, 2003; Kalinic & Marinkovic, 2016). Therefore, the following hypotheses are proposed: H3a: Perceived risks of online shopping negatively influence the repurchase intention. H3b: Perceived risks of online shopping negatively influence perceived usefulness. 2.4. Satisfaction As defined by Anderson and Srinivasan (2003), e-satisfaction is a customer’s contentment regarding his or her previous purchasing experience with a particular e-commerce company. Ha (2012) defines satisfaction as a consumer’s emotional reaction to a specific experience with a website. When purchasers are satisfied with online purchasing based on their previous experiences, they are more likely to buy again from the channel(s; Wen et al., 2011; Yoon, 2002), even with a higher frequency (C. Kim et al., 2012). For those reasons, the following hypothesis is formulated: H4: Satisfaction positively influences the repurchase intention. 2.5. Environmental awareness (EA) Environmental awareness can be defined as the knowledge of, and concerns about the influences of human activities on climate and environment together with thoughts and attitudes towards measures to solve environmental problems and improve environmental conditions (Hopwood et al., 2005; Schuitema et al., 2013). Many researches highlight that individuals with higher environmental awareness are more inclined to take (more) environmentally friendly actions (S.- C. Chen & Hung, 2016). According to Siikavirta et al. (2008), online shopping using home delivery services can significantly reduce the number of deliveries compared to customers picking up the goods by themselves. Depending on the door-to-door delivery methods used, greenhouse gas emissions can be mitigated by 18–87% while travel distance can reduce by just 54% to 93%, leading to decreases Figure 2. The theoretical framework of the study. Nguyen Thi et al., Cogent Business & Management (2022), 9: 2143016 https://doi.org/10.1080/23311975.2022.2143016 Page 5 of 25 in NOx, and PM10 (Buldeo Rai, 2021; Jaller & Pahwa, 2020; Wygonik & Goodchild, 2018). In this sense, online shopping can be considered an act of fostering environmental sustainability. Therefore, it can be predicted that individuals with a greater degree of environmental awareness might have a higher intention to shop online. Based on the above, the following hypothesis is proposed: H5: Environmental awareness positively influences the repurchase intention. 2.6. Perceived risk of COVID-19 (PR_COVID) According to Xie et al. (2019), “COVID-19 risk perception” is measured as an index, including affection and awareness to provide an overall measure of perceived risk. The rise of COVID-19 cases and the fear of infection coupled with health consequences affect the decision on various activities, such as shopping channels (Hieu, 2021; Loxton et al., 2020; Nguyen, 2021; Nguyen & Pojani, 2021, 2022a; Nguyen et al., 2021b; Nguyen-Phuoc et al., 2022a, 2022b; Tran et al., 2022). Most studies conducted in the COVID-19 era highlight that perceived risk of the pandemic is responsible for a shift from conventional shopping to online shopping (Eger et al., 2021; Nguyen et al., 2021a). A report by UNCTAD (2020a) shows that online purchases have increased by 6% to 10% across most product categories. Thus, the following hypothesis is preferred: H6: Perceived risk of COVID-19 positively influences the repurchase intention. 2.7. Personalization (PER) Consumers, especially Gen Z and Gen Y, not only enjoy more personalized products but are also willing to pay a premium for products that highlight their individuality (Francis & Hoefel, 2018). Personalization is defined as a customer’s perception of the extent to which an online store offers differentiated services to satisfy specific individual needs (Parasuraman et al., 1988; Yang & Jun, 2002). Previous research indicates that increased personalization of websites will promote customer loyalty to web-based services (T. (Catherine) Zhang et al., 2011). As for e-commerce, business’ personalization of services to suit customers’ needs, values, habits and lifestyles can help foster the relationships with customers and promote their loyalty, which originates from their repurchase intention (Harris & Goode, 2004). According to T.-P. Liang et al. (2009), personalized services have a higher positive impact on perceived usefulness than non-personalized services. A process tailored to the specific needs of an individual can enable them to be familiar with it, thus using it straightforwardly. As demonstrated by Kang and Namkung (2019), personalization which tailors technology environments for alignment with individual needs would increase consumers’ perceived ease of use. In addition, customers’ experience personalization is found to foster a higher customer satisfaction than standardized encounters (Bettencourt & Gwinner, 1996). Accordingly, the following hypotheses are proposed: H7a: Personalization positively influences the repurchase intention. H7b: Personalization positively influences perceived ease of use. H7c: Personalization positively influences perceived usefulness. H7d: Personalization positively influences satisfaction. Nguyen Thi et al., Cogent Business & Management (2022), 9: 2143016 https://doi.org/10.1080/23311975.2022.2143016 Page 6 of 25 3. Data collection and analytical methods 3.1. Research context Hanoi, the capital of Vietnam, is in North Vietnam and plays the role of a major center for economic development and international transactions, as well as a development engine of the Red River Delta and the whole country. With a population density of more than 2,455 inhabitants per square kilometer (Statista, 2021), Hanoi is the second most crowded and motorcycle-oriented city of Vietnam (H.N. Nguyen et al., 2020; Nguyen & Armoogum, 2020; Thanh Chuong & Minh Hieu, 2022). Before the COVID-19 pandemic, according to Vietnam E-Commerce Association (Vietnam E-Commerce Association, 2020), Hanoi ranked second after Ho Chi Minh City in terms of e-commerce index ranking. During the pandemic, online shopping was one of the most important shopping channels when the city applied social distancing periods, such as in April 2020 (Nguyen et al., 2021a). According to VietnamPlus (2021), citizens in the capital increased their use of online channels by 30% to 50%. 3.2. Survey design Based on a rigorous synthesis of the literature, a three-part self-administered questionnaire was designed. ●Its first component was a brief introduction that explained the survey’s goals and scope. ●The second section requested demographic information of the participants (e.g., gender, generation, income, educational level, living area). ●The last part encompassed 33 items to measure eight latent constructs. For “repurchase intention”, 4 items were adopted based on the studies of Wen et al. (2011) and Mohamed et al. (2014) while “perceived usefulness” was assessed using 4 items adapted from Pandey and Parmar (2019), p. 1 item modified from Davis (1989), and 1 item developed by the authors. “Perceived ease of use” was obtained through 3 items introduced by Gefen (2003). As for “perceived risks of online shopping”, 4 items were utilized based on Masoud (2013), Ariff et al. (2014). 4 items of “satisfaction” were accessed from Janda et al. (2002), Seiders et al. (2005) and simultaneously, 4 items of “environmental awareness” were derived from Y. Wang et al. (2018). “Perceived risk of COVID-19” was measured using 3 items of Dryhurst et al. (2020) and Brewer and Sebby (2021), and 2 items developed by the authors. “Personalization” was assessed using 2 items based on Wolfinbarger and Gilly (2003) and 1 item derived from D.N. Su et al. (2022). All measurement scales were evaluated on a five-point Likert scale ranging from 1 (= “strongly disagree”) to 5 (= “strongly agree”). First, a paper-based questionnaire in English was developed. Subsequently, the questions and scales were converted into Vietnamese. Based on the suggestions of two researchers with relevant expertise, the precise translation and wording of items in the local context were completed. Next, it was tested on a sample of 20 respondents (10 for each generation), who pointed out any unclear words or probable survey difficulties, allowing for an additional questionnaire development. Finally, the questionnaire was eligible for the official survey after two rounds of testing to enhance both of its validity and reliability. 3.3. Data collection and sample To collect data for this research, a survey was carried out within three weeks, from 28 th February to 20 March 2022 in Hanoi. During this period, the daily number of COVID-19 infections was relatively high at around 2,000. However, there were no mobility restrictions imposed by the government. Working and studying activities took place normally as pre-COVID-19. Since the number of online shopping users in Hanoi remained unknown, we applied a convenience sampling technique. Specifically, twelve surveyors, after being trained carefully, were divided into six groups, each of which took responsibility for two urban and three non-urban districts. In each district, surveyors worked 5 shifts in different public locations (e.g., department stores, supermarkets, universities, restaurants, apartments, and walking streets). Each shift lasted two hours, one of which was for surveying Gen Y while another was for surveying Gen Z. At a survey location, each surveyor randomly asked a person to participate in the survey. In case of achieving a participation approval Nguyen Thi et al., Cogent Business & Management (2022), 9: 2143016 https://doi.org/10.1080/23311975.2022.2143016 Page 7 of 25 4.2.4. Moderating effect In PLS-SEM, the multigroup analysis (MGA), known as PLS-MGA, is a technique for assessing whether hypothesized relationships vary significantly across groups (Sarstedt et al., 2011). This study applied PLSMGA analysis using the bootstrapping method to investigate the moderating effects of generations on the relationships between the predictors and repurchase intention of online consumers (i.e., answering the third research question set in Section 1). Before conducting the PLS-MGA, the measurement invariance test was performed (Matthews, 2017). A p-value greater than 0.95 or less than 0.05 in Henseler’s PLS-MGA approach reveals the significant differences between specific PLS path coefficients across two groups at the 5% level of significance. As reported in Table 8, only the relationships between perceived risks of online shopping and repurchase intention was significantly different between Gen Y and Gen Z groups. Notably, the negative impact of perceived risks of online shopping on repurchase intention is significantly larger for Gen Y (β = −0.155, p = 0.000) compared to Gen Z. 5. Discussions 5.1. Theoretical implications Sharing the same view with Alalwan et al. (2018), Bhattacherjee (2001), Lee and Chang (2011), the results of this study confirmed that when customers assume that a technology-based service is more useful, they are more likely to have a higher level of repurchase intention. This study corroborated previous findings that perceived ease of use had an indirect effect on the behavioral intention via perceived usefulness (L. Chen & Aklikokou, 2020). However, in contradiction with Chuan-Chuan Lin and Lu (2000), we also found that perceived ease of use had a direct positive effect on the behavioral intention. Notably, Amin et al. (2014b) and Liu et al. (2016) supported our findings that an effortless e-shopping system leads to not only a greater intention to repurchase but also a higher perception of usefulness. Since both Gen Y and Z were born into the digital era, they are well aware of using technologies for online purchasing and its benefits (Nicholas, 2009), resulting in no generational differences in the effects of perceived usefulness and perceived ease of use on continuance intention. Initially, supported by the study of Pappas et al. (2012), we supposed that personalization would directly affect repurchase intention. Nevertheless, this study was unsuccessful in proving this. A possible explanation would be that personalization may coincide with the concern about privacy invasion (Arpaci, 2016), which is a significant problem in developing countries due to the lack of resources to enforce legislation meant to protect e-users (UNCTAD, 2020b). The concern would prevent the respondents from considering personalization as a direct facilitator of repurchase intention. Meanwhile, consistent with Kwon and Kim (2012) and T.-P. Liang et al. (2009), personalization was found to have substantial positive mediating impacts on repurchase intention via perceived ease of use, perceived usefulness, and satisfaction. These results demonstrated that customers highly appreciated the outcomes generated by personalization when shaping their repurchase intention. This research affirmed the negative effect of perceived risks of online shopping on repurchase intention as also proposed by H.-F. Chen and Chen (2019), L.J. Liang et al. (2018). Moreover, it Table 5. Evaluation of predictive accuracy and predictive relevance Factors R 2 Predictive capability level Q 2 Predictive relevance level PEOU 0.343 Moderate 0.260 Medium PU 0.494 Moderate 0.336 Medium RI 0.540 Moderate 0.388 Medium SAT 0.346 Moderate 0.262 Medium Nguyen Thi et al., Cogent Business & Management (2022), 9: 2143016 https://doi.org/10.1080/23311975.2022.2143016 Page 14 of 25 Table 6. Direct, indirect, and total effects Paths Direct effects Total indirect effects Total effects β Std. p H Result β Std. p β Std. p PU → RI 0.268*** 0.049 0.000 H1 Support 0.268*** 0.049 0.000 PEOU → RI 0.216*** 0.041 0.000 H2a Support 0.080*** 0.018 0.000 0.296*** 0.039 0.000 PEOU → PU 0.299*** 0.041 0.000 H2b Support 0.299*** 0.041 0.000 PR_OS → RI −0.100** 0.038 0.005 H3a Support −0.022* 0.009 0.011 −0.122** 0.041 0.002 PR_OS → PU −0.083** 0.031 0.004 H3b Support −0.083** 0.031 0.004 SAT → RI 0.358*** 0.040 0.000 H4 Support 0.358*** 0.040 0.000 EA → RI 0.075* 0.036 0.038 H5 Support 0.075* 0.036 0.038 PR_COVID → RI −0.005 ns 0.032 0.876 H6 Reject −0.005 ns 0.032 0.876 PER → RI −0.047 ns 0.041 0.251 H7a Reject 0.516*** 0.033 0.000 0.468*** 0.037 0.000 PER → PEOU 0.586*** 0.028 0.000 H7b Support 0.586*** 0.028 0.000 PER → PU 0.491*** 0.038 0.000 H7c Support 0.175*** 0.026 0.000 0.666*** 0.027 0.000 PER → SAT 0.589*** 0.030 0.000 H7d Support 0.589*** 0.030 0.000 Note: ***p < 0.001; **p < 0.01; *p < 0.05; ns: non-significant. Nguyen Thi et al., Cogent Business & Management (2022), 9: 2143016 https://doi.org/10.1080/23311975.2022.2143016 Page 15 of 25 furthered the literature by illustrating the greater negative impact of perceived risks on Gen Y’s repurchase intention than on Gen Z’s. Our finding was a response to the limitation of TAM claimed by Venkatesh et al. (2003) that investigating more representative moderator variables beyond demographics is essential. Indeed, generations refer to not only age but also distinct traits and lifestyles (Dahlberg et al., 2015; Lissitsa & Kol, 2021). Gen Y pays more attention to the risks possibly because of their greater purchasing power and higher frequency of placing big orders, making them more sensitive to risks (Hoffower, 2021). As reported by Merriman (2015), Gen Y has a strong desire for financial safety since they experienced significant uncertainty in their life as a result of the financial crisis and subsequent recessions. In contrast, Gen Z consumers, who are studying or just entering the labor market, usually have a limited financial budget, leading to their wide-ranging comparison among e-stores. This group of consumers may even be willing to choose e-stores offering low prices and accept high(er) risks in parallel. As expected, satisfaction was found to be a driving factor of repurchase intention; whereas, surprisingly, this study failed to find any significant correlation between perceived COVID-19related risk and repurchase intention. This contradictory result could be explained that respondents were familiar with and ready to live in the new normal with the presence of the coronavirus. By February 2022, the time of this research survey, over 75% of Vietnamese were fully vaccinated (Statista, 2022b) and the rate was even higher in Hanoi (WHO Viet Nam, 2022). Previous Figure 3. The path analysis results. Note: ***p < 0.001; **p < 0.01; *p < 0.05; ns: non-significant; dotted line represents insignificant path. Table 7. Specific indirect effect Paths β Std. p PEOU → PU → RI 0.080*** 0.018 0.000 PR_OS → PU → RI −0.022* 0.009 0.011 PER → PEOU → RI 0.127*** 0.025 0.000 PER → PEOU → PU 0.175*** 0.026 0.000 PER → PEOU → PU → RI 0.047*** 0.011 0.000 PER → PU → RI 0.131*** 0.026 0.000 PER → SAT → RI 0.211*** 0.026 0.000 ***p < 0.001; *p < 0.05 Nguyen Thi et al., Cogent Business & Management (2022), 9: 2143016 https://doi.org/10.1080/23311975.2022.2143016 Page 16 of 25 Table 8. Multigroup analysis results Relationship Gen Z Gen Y β Difference (β 1 —β 2 )p-value Difference (OneTailed) Supported β 1 p-value β 2 p-value PU → RI 0.209 0.002 0.287 0.000 −0.078 0.788 No PEOU → RI 0.203 0.000 0.221 0.000 −0.018 0.591 No PR_OS → RI −0.029 0.583 −0.155 0.000 0.126 0.021 Yes SAT → RI 0.391 0.000 0.334 0.000 0.057 0.238 No EA → RI 0.058 0.347 0.087 0.071 −0.029 0.645 No PR_COVID → RI 0.031 0.497 −0.027 0.532 0.059 0.178 No PER → RI −0.022 0.737 −0.071 0.197 0.049 0.287 No Nguyen Thi et al., Cogent Business & Management (2022), 9: 2143016 https://doi.org/10.1080/23311975.2022.2143016 Page 17 of 25 researchers, such as Lee et al. (2012), Nguyen and Pojani (2022b), report the insignificant effects of the perceived risk of the pandemics (i.e., H1N1 and COVID-19) on the use or the usage intention of tourism and transportation modes when the diseases were well-controlled. This research showed that when online shoppers comprehended environmentally friendly characteristics of e-commerce platforms, they tended to repurchase items on online platforms. It is noticeable that the technology adoption literature only records the moderation effect of environmental awareness between attitude and intention to use e-services (e.g., Chauhan et al., 2021; Shah et al., 2021), whereas, the direct association of environmental perception with e-purchasing intention has not been explored yet. Therefore, the finding of this current research has broadened the related literature of eco-friendly virtual shopping. The insignificant moderating effect of generations on the relationship between environmental awareness and repurchase intention would be more or less surprising because Gen Z tends to have a strong concentration on environmental matters (C.-H. (Joan) Su et al., 2019). However, it is understandable since both Gen Y and Gen Z are recognized to be environmentally conscious (McCrindle & Wolfinger, 2009). Recent empirical comparative evidence, such as (Casalegno et al., 2022), highlights that environmental concern is a strong antecedent of sustainable purchase behaviors for both Gen Y and Z. In addition, environmental perception is well demonstrated to have positive impacts on Gen Y’s and Gen Z’s green behaviors in emerging countries (Ogiemwonyi, 2022; Saut & Saing, 2021). Another possible interpretation is involved in the shared and increasing concerns about the poor air quality and pollutions in Hanoi (VnExpress, 2019). This could result in similar effects of environmental awareness on the intention to keep purchasing virtually between the two generations. 5.2. Managerial implications The findings of this study are essential for marketing managers and e-commerce platforms. The insignificant role of the perceived pandemic-related risk demonstrated that the e-commerce sector is actually in the new normal wherein consumers are no longer concerned about COVID19 when choosing shopping channels. Therefore, marketing programs and initiatives should focus on factors that have been consistently proved as the influential factors of e-shopping intentions before the presence of COVID-19 pandemic (e.g., perceived usefulness, satisfaction, perceived risks of online shopping). Specifically, it is crucial for website planners and e-commerce sellers to keep a user-friendly and informative purchasing process through clear explanations, exciting and pleasant interface to foster positive feelings in users. The promotion of personalization, hence, would contribute to this goal. For sellers, it could be gained through the adoption of personalized marketing, promotion, and communication strategy. For online shopping platforms, enhanced personalization features may be obtained through investment in customer data and analytics foundation, as well as the acquisition of technology talents. However, possible threats of personalization should also be considered to enhance customer’s sense of safety when experiencing online shopping and providing their personal information. Demonstrating online shopping as a green activity through using electric bikes or motorcycles in the last-mile delivery procedure or showing the environmental responsibility of stakeholders can be a wise way to retain e-shoppers. Designing and operating customer services effectively to gather customers’ response regarding their online shopping experience are critical to evaluate customers’ satisfaction and implement improvements. Online shopping is a process attributable to both online and offline procedures. The quality of products and offline processes, which involve good delivery to customers, should be controlled strictly to maintain a superior service. Specifically, complete and accurate product information, along with trustworthy and authentic reviews from previous buyers should be readily available in order that customers can easily visualize the actual quality of the product and avoid misunderstandings or unrealistic expectations. In addition, the return and refund policy should be concisely and clearly designed to provide consumers with a feeling of security against product-related risks or financial loss. Moreover, since Gen Y users pay more attention to the online shopping risks, Nguyen Thi et al., Cogent Business & Management (2022), 9: 2143016 https://doi.org/10.1080/23311975.2022.2143016 Page 18 of 25 companies’ solutions and policies of risk management should be informed more intensively for this subject. 6. Conclusions Our study has enhanced the understanding of the determinants of the continuance intention to shop online of Generation Y and Z when COVID-19 is well controlled. The present research found that facilitators of repurchase intention included perceived usefulness, perceived ease of use, satisfaction, and environmental awareness while perceived risks of online shopping served as a barrier. Notably, the barrier was found to be significantly larger to the repurchase intention of Gen Y. Personalization was not directly associated with the intention but had strong indirect effects through perceived usefulness, perceived ease of use, and satisfaction. The risk of COVID-19 was not a predictor of repurchase intention of online shopping during the new normal. Based on the findings of influential factors, a series of managerial implications were proposed. Although this research was designed and carried out rigorously, its findings need to be clarified with attention to some following limitations. First, the convenience sampling approach would result in biases. Although it is understandable that users living in urban areas are more inclined to online purchase (Shao et al., 2022; Zhou & Wang, 2014), they seemed overrepresented in this study. Although the data used were not representative perfectly, we nevertheless believe that a large sample of nearly 850 responses collected in accordance with a carefully designed recruitment strategy (see, subsection 3.3) could enable to attain useful results. Second, online shopping is a complex behavior possibly affected by a wide range of factors, some of which were disregarded in this study. Therefore, the combination with other well-known models, such as the Theory of Planned Behavior and Risk Theory, to extend our proposed theoretical framework is necessary to better model the continuance intention. Third, due to regional differences in the growth of e-commerce, the COVID-19 progression, and strategies in response to the pandemic, our results need to be validated and extended by replicating this study in other geographical areas. Acknowledgements This research is funded by the Foreign Trade University under the research program number FTURP02-2020-10. The authors would like to thank the editor and two anonymous reviewers for useful advice. Funding This work was supported by the Foreign Trade University [FTURP02-2020-10]. Author details Binh Nguyen Thi 1 ORCID ID: http://orcid.org/0000-0002-2813-2967 Thi Lan Anh Tran 2 Thi Thu Hien Tran 23 Thanh Thao Le 2 Phan Nhat Hang Tran 2 Minh Hieu Nguyen 3 E-mail: [email protected] ORCID ID: http://orcid.org/0000-0003-4863-5446 1 School of Economics and International Business, Foreign Trade University, Hanoi,Vietnam. 2 Foreign Trade University, Hanoi, Vietnam. 3 Faculty of Transport-Economics, University of Transport and Communications, Hanoi, Vietnam. Disclosure statement No potential conflict of interest was reported by the author(s). Author contributions The authors confirm contribution to the paper as follows: Study conception and design, B.N.T., M.H.N; data collection, T.L.A.T., T.T.H.T., T.T.L., P.N.H.T; analysis and interpretation of results: B.N.T., M.H.N., T.T.H.T; draft manuscript preparation: T.L.A.T., T.T.L., P.N.H.T., B.N.T; critical revision of the paper: M.H.N., B.N.T. All authors have read and agreed to the published version of the manuscript. Citation information Cite this article as: Factors influencing continuance intention of online shopping of generation Y and Z during the new normal in Vietnam, Binh Nguyen Thi, Thi Lan Anh Tran, Thi Thu Hien Tran, Thanh Thao Le, Phan Nhat Hang Tran & Minh Hieu Nguyen, Cogent Business & Management (2022), 9: 2143016. References Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in Human Behavior, 63, 75–90. https://doi. org/10.1016/j.chb.2016.05.014 Alalwan, A. A., Baabdullah, A. M., Rana, N. P., Tamilmani, K., & Dwivedi, Y. K. (2018). Examining adoption of mobile internet in Saudi Arabia: Extending TAM with perceived enjoyment, innovativeness and trust. Technology in Society, 55, 100–110. https://doi.org/10.1016/j.techsoc.2018.06. 007 Al-Hattami, H.M. (2021). Determinants of intention to continue usage of online shopping under a pandemic: Covid-19. Cogent Business & Management, 8 (1), 1936368. https://doi.org/10.1080/23311975. 2021.1936368 Alyoubi, A. A., 2015. E-commerce in developing countries and how to develop them during the introduction of Nguyen Thi et al., Cogent Business & Management (2022), 9: 2143016 https://doi.org/10.1080/23311975.2022.2143016 Page 19 of 25 modern systems. Procedia Computer Science, International Conference on Communications, management, and Information technology (ICCMIT’2015) 65, 479–483. Prague, Czech Repulic. https://doi.org/ 10.1016/j.procs.2015.09.127 Amin, M., Rezaei, S., & Abolghasemi, M. (2014b). User satisfaction with mobile websites: The impact of perceived usefulness (PU), perceived ease of use (PEOU) and trust. Nankai Business Review International, 5(3), 258–274. https://doi.org/10.1108/ NBRI-01-2014-0005 Anderson, R. E., & Srinivasan, S. S. (2003). E-satisfaction and e-loyalty: A contingency framework. Psychology & Marketing, 20(2), 123–138. https://doi.org/10.1002/ mar.10063 Andreev, P., Salomon, I., & Pliskin, N. (2010). Review: state of teleactivities. transportation research part C: emerging technologies. Information/Communication Technologies and Travel Behaviour, 18, 3–20. https:// doi.org/10.1016/j.trc.2009.04.017 Ariff, M. S. M., Sylvester, M., Zakuan, N., Ismail, K., & Ali, K. M. (2014). Consumer perceived risk, attitude and online shopping behaviour$\mathsemicolon$ empirical evidence from Malaysia. IOP Conf. Ser.: Mater. Sci. Eng, 58, 012007. https://doi.org/10.1088/ 1757-899X/58/1/012007 Arpaci, I. (2016). Understanding and predicting students’ intention to use mobile cloud storage services. Computers in Human Behavior, 58, 150–157. https:// doi.org/10.1016/j.chb.2015.12.067 Astrachan, C. B., Patel, V. K., & Wanzenried, G. (2014). A comparative study of CB-SEM and PLS-SEM for theory development in family firm research. Journal of Family Business Strategy, Innovative and Established Research Methods in Family Business, 5 (1), 116–128. https://doi.org/10.1016/j.jfbs.2013.12. 002 Babenko, V., Kulczyk, Z., Perevosova, I., Syniavska, O., Davydova, O., Semerikov, S., Soloviev, V., Kibalnyk, L., Chernyak, O., & Danylchuk, H. (2019). Factors of the development of international e-commerce under the conditions of globalization. SHS Web Conf, 65, 04016. https://doi.org/10.1051/shsconf/20196504016 Bettencourt, L. A., & Gwinner, K. (1996). Customization of the service experience: The role of the frontline employee. International Journal of Service Industry Management, 7(2), 3–20. https://doi.org/10.1108/ 09564239610113442 Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/ 10.2307/3250921 Brewer, P., & Sebby, A. G. (2021). The effect of online restaurant menus on consumers’ purchase intentions during the COVID-19 pandemic. International Journal of Hospitality Management, 94, 102777. https://doi.org/10.1016/j.ijhm.2020.102777 Brown, S. A., Massey, A. P., Montoya-weiss, M. M., & Burkman, J. R. (2002). Do I really have to? User acceptance of mandated technology. European Journal of Information Systems, 11(4), 283–295. https://doi.org/10.1057/palgrave.ejis.3000438 Buldeo Rai, H. (2021). The net environmental impact of online shopping, beyond the substitution bias. Journal of Transport Geography, 93, 103058. https:// doi.org/10.1016/j.jtrangeo.2021.103058 Casalegno, C., Candelo, E., & Santoro, G. (2022). Exploring the antecedents of green and sustainable purchase behaviour: A comparison among different generations. Psychology & Marketing, 39(5), 1007–1021. https://doi.org/10.1002/mar.21637 Chauhan, H., Pandey, A., Mishra, S., & Rai, S. K. (2021). Modeling the predictors of consumers’ online purchase intention of green products: The role of personal innovativeness and environmental drive. Environ Dev Sustain, 23(11), 16769–16785. https:// doi.org/10.1007/s10668-021-01337-9 Chen, L., & Aklikokou, A. K. (2020). Determinants of E-government adoption: testing the mediating effects of perceived usefulness and perceived ease of use. International Journal of Public Administration, 43 (10), 850–865. https://doi.org/10.1080/01900692. 2019.1660989 Chen, H.-F., & Chen, S.-H., 2019. How website quality, service quality, perceived risk and customer satisfaction affects repurchase intension? A case of Taobao online shopping, in: Proceedings of the 10th International Conference on E-Education, E-Business, E-Management and E-Learning, IC4E ’19. Association for Computing Machinery, New York, NY, USA, pp. 326–328. ACM Digital Library. https://doi.org/10. 1145/3306500.3306571 Chen, S.-C., & Hung, C.-W. (2016). Elucidating the factors influencing the acceptance of green products: An extension of theory of planned behavior. Technological Forecasting and Social Change, 112, 155–163. https://doi.org/10.1016/j.techfore.2016.08. 022 Chiu, C., Chang, C., Cheng, H., & Fang, Y. (2009). Determinants of customer repurchase intention in online shopping. Online Information Review, 33(4), 761–784. https://doi.org/10.1108/ 14684520910985710 Chuan-Chuan Lin, J., & Lu, H. (2000). Towards an understanding of the behavioural intention to use a web site. International Journal of Information Management, 20(3), 197–208. https://doi.org/10. 1016/S0268-4012(00)00005-0 Cunningham, S. M. (1967). The major dimensions of perceived risk. In Cox, D.F. (Ed.), Risk taking and information handling in consumer behaviour (pp. 82–108). Boston Graduate School of Business Administration, Harvard University Press. Dabija, D.-C., & Lung, L. (2019) Millennials versus gen z: online shopping behaviour in an emerging market. In Applied ethics for entrepreneurial success: recommendations for the developing world, springer proceedings in business and economics S. Văduva, I. Fotea, L. P. Văduva, & R. Wilt Eds. Springer International Publishing 1–18. https://doi.org/10. 1007/978-3-030-17215-2_1 Dahlberg, T., Guo, J., & Ondrus, J. (2015). A critical review of mobile payment research. Electronic Commerce Research and Applications, Contemporary Research on Payments and Cards in the Global Fintech Revolution, 14, 265–284. https://doi.org/10.1016/j. elerap.2015.07.006 Davis, F. D., 1985. A technology acceptance model for empirically testing new end-user information systems : theory and results (Thesis). Massachusetts Institute of Technology. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https:// doi.org/10.2307/249008 Dimock, M., 2019. Defining generations: Where Millennials end and Generation Z begins. Pew Research Center. URL https://www.pewresearch.org/fact-tank/2019/ 01/17/where-millennials-end-and-generation -z-begins/ (accessed 5.3.22) Dryhurst, S., Schneider, C. R., Kerr, J., Freeman, A. L. J., Recchia, G., van der Bles, A. M., Spiegelhalter, D., & Nguyen Thi et al., Cogent Business & Management (2022), 9: 2143016 https://doi.org/10.1080/23311975.2022.2143016 Page 20 of 25 van der Linden, S. (2020). Risk perceptions of COVID-19 around the world. Journal of Risk Research, 23(7–8), 994–1006. https://doi.org/10.1080/ 13669877.2020.1758193 E-Commerce Association, V, 2020. Vietnam E-Business Index 2020 Report [WWW Document]. URL http://en. vecom.vn/vietnam-e-business-index-2020-report-1 (accessed 5.4.22). Eger, L., Komárková, L., Egerová, D., & Mičík, M. (2021). The effect of COVID-19 on consumer shopping behaviour: Generational cohort perspective. Journal of Retailing and Consumer Services, 61, 102542. https://doi.org/10.1016/j.jretconser.2021.102542 Fauzi, A. A., & Sheng, M. L. (2020). Ride-hailing apps’ continuance intention among different consumer groups in Indonesia: The role of personal innovativeness and perceived utilitarian and hedonic value. Asia Pacific Journal of Marketing and Logistics, 33(5), 1195–1219. https://doi.org/10.1108/APJML-05-2019-0332 Featherman, M. S., & Pavlou, P. A. (2003) Predicting e-services adoption: A perceived risk facets perspective. International Journal of HumanComputer Studies, 59(4), 451–474. Zhang and Dillon Special Issue on HCI and MIS. https://doi.org/10. 1016/S1071-5819(03)00111-3 F. Hair, J., Jr, Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR10-2013-0128 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/10.2307/3151312 Francis, T., & Hoefel, F., 2018. Generation Z characteristics and its implications for companies | McKinsey [WWW Document]. URL https://www.mckinsey.com/indus tries/consumer-packaged-goods/our-insights/truegen-generation-z-and-its-implications-forcompanies (accessed 5.3.22) Gefen, D. (2003). TAM or just plain habit: A look at experienced online shoppers. JOEUC, 15, 1–13. https://doi.org/10.4018/joeuc.2003070101 Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: an integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/ 30036519 Ha, H.-Y. (2012). The effects of online shopping attributes on satisfaction–purchase intention link: A longitudinal study. International Journal of Consumer Studies, 36(3), 327–334. https://doi.org/10. 1111/j.1470-6431.2011.01035.x Hair, J. F. (Ed.). (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (Second edition. ed.). Sage. 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 Hamid, A. A., Razak, F. Z. A., Bakar, A. A., & Abdullah, W. S. W., 2016. The effects of perceived usefulness and perceived ease of use on continuance intention to use E-government. Procedia Economics and Finance, 7th International Economics & Business Management Conference (IEBMC 2015) 35, 644–649. Kuantan, Malaysia. https://doi.org/10. 1016/S2212-5671(16)00079-4 Han, B. R., Sun, T., Chu, L. Y., Wu, L., & Han, B. R. (2022). COVID-19 and E-commerce operations: evidence from Alibaba. Manufacturing & Service Operations Management, 8(1). https://doi.org/10.1287/msom. 2021.1075 Harris, L. C., & Goode, M. M. H. (2004). The four levels of loyalty and the pivotal role of trust: A study of online service dynamics. Journal of Retailing, 80(2), 139–158. https://doi.org/10.1016/j.jretai.2004.04.002 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 Hieu, N. M. (2021). Modelling school travel mode choice – The case of Hanoi. Vietnam. Transport and Communications Science Journal, 72(7), 778–788. https://doi.org/10.47869/tcsj.72.7.1 Hoffower, H., 2021. Gen Z and millennials actually want the same things at work. But Gen Z has the upper hand. [WWW Document]. Business Insider. URL https://www.businessinsider.com/millennials-versusgen-z-workplace-trends-flexibility-work-life-balance -2021-11 (accessed 5.17.22) Hopwood, B., Mellor, M., & O’Brien, G. (2005). Sustainable development: Mapping different approaches. Sustainable Development, 13(1), 38–52. https://doi. org/10.1002/sd.244 Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118 Hubert, M., Blut, M., Brock, C., Backhaus, C., & Eberhardt, T. (2017). Acceptance of smartphone-based mobile shopping: mobile benefits. Customer Characteristics, Perceived Risks, and the Impact of Application Context. Psychology & Marketing, 34, 175–194. https://doi.org/10.1002/mar.20982 Jaller, M., & Pahwa, A. (2020). Evaluating the environmental impacts of online shopping: A behavioral and transportation approach. Transportation Research Part D: Transport and Environment, 80, 102223. https://doi.org/10.1016/j.trd.2020.102223 Janda, S., Trocchia, P. J., & Gwinner, K. P. (2002). Consumer perceptions of Internet retail service quality. International Journal of Service Industry Management, 13(5), 412–431. https://doi.org/10. 1108/09564230210447913 Kalinic, Z., & Marinkovic, V. (2016). Determinants of users’ intention to adopt m-commerce: An empirical analysis. Inf Syst E-Bus Manage, 14(2), 367–387. https:// doi.org/10.1007/s10257-015-0287-2 Kang, J.-W., & Namkung, Y. (2019). The role of personalization on continuance intention in food service mobile apps: A privacy calculus perspective. International Journal of Contemporary Hospitality Management, 31(2), 734–752. https://doi.org/10. 1108/IJCHM-12-2017-0783 Khalifa, M., & Liu, V. (2007). Online consumer retention: Contingent effects of online shopping habit and online shopping experience. Eur J Inf Syst, 16(6), 780–792. https://doi.org/10.1057/palgrave.ejis.3000711 Khan, G. F., Sarstedt, M., Shiau, W.-L., Hair, J. F., Ringle, C. M., & Fritze, M. P. (2019). Methodological research on partial least squares structural equation modeling (PLS-SEM): An analysis based on social network approaches. Internet Research, 29(3), 407–429. https://doi.org/10.1108/IntR-12-2017-0509 Kim, C., Galliers, R. D., Shin, N., Ryoo, J.-H., & Kim, J. (2012). Factors influencing Internet shopping value and customer repurchase intention. Electronic Commerce Research and Applications, 11(4), 374–387. https:// doi.org/10.1016/j.elerap.2012.04.002 Nguyen Thi et al., Cogent Business & Management (2022), 9: 2143016 https://doi.org/10.1080/23311975.2022.2143016 Page 21 of 25 Kim, G. S., Park, S.-B., & Oh, J. (2008). An examination of factors influencing consumer adoption of short message service (SMS). Psychology & Marketing, 25 (8), 769–786. https://doi.org/10.1002/mar.20238 Koch, J., Frommeyer, B., & Schewe, G. (2020). Online Shopping Motives during the COVID-19 Pandemic— Lessons from the Crisis. Sustainability, 12(24), 10247. https://doi.org/10.3390/su122410247 Ko, H., Jung, J., Kim, J., & Shim, S. W. (2004). Crosscultural differences in perceived risk of online shopping. Journal of Interactive Advertising, 4(2), 20–29. https://doi.org/10.1080/15252019.2004. 10722084 Kok Wai, Dastane, D. O., Johari, Z., & Ismail, N. B., 2019. Perceived risk factors affecting consumers’ online shopping behaviour (SSRN Scholarly Paper No. 3498766). Social Science Research Network, Rochester, NY. https://doi.org/10.2139/ssrn.3498766 Kshetri, N. (2007). Barriers to e-commerce and competitive business models in developing countries: A case study. Electronic Commerce Research and Applications, Intelligent Agents in E-services, 6(4), 443–452. https:// doi.org/10.1016/j.elerap.2007.02.004 Kwon, K., & Kim, C. (2012). How to design personalization in a context of customer retention: Who personalizes what and to what extent? Electronic Commerce Research and Applications, the Role of Business Analytics in E-Commerce, 11(2), 101–116. https://doi. org/10.1016/j.elerap.2011.05.002 Lawrence, J. E., & Tar, U. A. (2010). Barriers to e-commerce in developing countries. Information, Society and Justice Journal, 3, 23–35. https://reposi tory.londonmet.ac.uk/88/ Lee, -H.-H., & Chang, E. (2011). Consumer attitudes toward online mass customization: an application of extended technology acceptance model. Journal of Computer-Mediated Communication, 16(2), 171–200. https://doi.org/10.1111/j.1083-6101.2010.01530.x Lee, C.-K., Song, H.-J., Bendle, L. J., Kim, M.-J., & Han, H. (2012). The impact of non-pharmaceutical interventions for 2009 H1N1 influenza on travel intentions: A model of goal-directed behavior. Tourism Management, 33(1), 89–99. https://doi.org/10.1016/j. tourman.2011.02.006 Liang, T.-P., Chen, H.-Y., & Turban, E., 2009. Effect of personalization on the perceived usefulness of online customer services: A dual-core theory, in: Proceedings of the 11th International Conference on Electronic Commerce, ICEC ’09. association for computing machinery, New York, NY, USA, pp. 279–288. ACM Digital Library. https://doi.org/10.1145/1593254. 1593296 Liang, L. J., Choi, H. C., & Joppe, M. (2018). Understanding repurchase intention of Airbnb consumers: Perceived authenticity, electronic word-of-mouth, and price sensitivity. Journal of Travel & Tourism Marketing, 35, 73–89. https://doi.org/10.1080/10548408.2016. 1224750 Lissitsa, S., & Kol, O. (2016). Generation X vs. generation Y – A decade of online shopping. Journal of Retailing and Consumer Services, 31, 304–312. https://doi.org/ 10.1016/j.jretconser.2016.04.015 Lissitsa, S., & Kol, O. (2021). Four generational cohorts and hedonic m-shopping: Association between personality traits and purchase intention. Electron Commer Res, 21(2), 545–570. https://doi.org/10.1007/ s10660-019-09381-4 Liu, Z., Shan, J., & Pigneur, Y. (2016). The role of personalized services and control: An empirical evaluation of privacy calculus and technology acceptance model in the mobile context. Journal of Information Privacy and Security, 12(3), 123–144. https://doi.org/ 10.1080/15536548.2016.1206757 Loxton, M., Truskett, R., Scarf, B., Sindone, L., Baldry, G., & Zhao, Y. (2020). Consumer behaviour during crises: preliminary research on how coronavirus has manifested consumer panic buying, herd mentality, changing discretionary spending and the role of the media in influencing behaviour. Journal of Risk and Financial Management, 13(8), 166. https://doi.org/10. 3390/jrfm13080166 Lu, H., Hsu, C., & Hsu, H. (2005). An empirical study of the effect of perceived risk upon intention to use online applications. Information Management & Computer Security, 13(2), 106–120. https://doi.org/10.1108/ 09685220510589299 Luo, X., Li, H., Zhang, J., & Shim, J. P. (2010). Examining multi-dimensional trust and multi-faceted risk in initial acceptance of emerging technologies: An empirical study of mobile banking services. Decision Support Systems, 49(2), 222–234. https://doi.org/10. 1016/j.dss.2010.02.008 Masoud, E. Y. (2013). The effect of perceived risk on online shopping in Jordan. European Journal of Business and Management, 5, 76–87. https://www.researchgate. net/publication/340438323_The_Effect_of_ Perceived_Risk_on_Online_Shopping_in_Jordan Matthews, L. (2017) Applying multigroup analysis in PLSSEM: A step-by-step process. In Partial least squares path modeling: basic concepts, methodological issues and applications H. Latan & R. Noonan Eds. Springer International Publishing 219–243. https://doi.org/10. 1007/978-3-319-64069-3_10 McCrindle, M., & Wolfinger, E., 2009. The ABC of XYZ: understanding the global generations. The ABC of XYZ. University of New South Wales Press. McKinsey & Company, 2020. A global view of how consumer behavior is changing amid COVID-19. Merriman, M., 2015. What if the next big disruptor isn’t a what but a who? Ernst & Young LLP. https://assets. ey.com/content/dam/ey-sites/ey-com/en_gl/topics/ digital/ey-rise-of-gen-z-new-challenge-for-retailers. pdf Mohamed, N., Hussein, R., Hidayah Ahmad Zamzuri, N., & Haghshenas, H. (2014). Insights into individual’s online shopping continuance intention. Industrial Management & Data Systems, 114(9), 1453–1476. https://doi.org/10.1108/IMDS-07-2014-0201 Morgan, J. P. (2021). Global E-Commerce Trends Report. JPMorgan. https://www.jpmorgan.com/content/dam/ jpm/treasury-services/documents/global-e-com merce-trends-report.pdf Natarajan, T., Balasubramanian, S. A., & Kasilingam, D. L. (2018). The moderating role of device type and age of users on the intention to use mobile shopping applications. Technology in Society, 53, 79–90. https://doi.org/10.1016/j.techsoc.2018.01.003 Nguyen, M. H. (2021). Factors influencing home-based telework in Hanoi (Vietnam) during and after the COVID-19 era. Transportation, 48(6), 3207–3238. https://doi.org/10.1007/s11116-021-10169-5 Nguyen, M. H., & Armoogum, J. (2020). Hierarchical process of travel mode imputation from GPS data in a motorcycle-dependent area. Travel Behaviour and Society, 21, 109–120. https://doi.org/10.1016/j.tbs. 2020.06.006 Nguyen, M. H., Armoogum, J., & Nguyen, T. B. (2021a). Factors affecting the growth of E-Shopping over the COVID-19 Era in Hanoi. Vietnam. Sustainability, 13 (16), 9205. https://doi.org/10.3390/su13169205 Nguyen-Phuoc, D. Q., Oviedo-Trespalacios, O., Nguyen, M. H., Dinh, M. T. T., & Su, D. N. (2022a). Nguyen Thi et al., Cogent Business & Management (2022), 9: 2143016 https://doi.org/10.1080/23311975.2022.2143016 Page 22 of 25 Intentions to use ride-sourcing services in Vietnam: What happens after three months without COVID-19 infections? Cities, 126, 103691. https://doi.org/10. 1016/j.cities.2022.103691 Nguyen-Phuoc, D. Q., Su, D. N., Nguyen, M. H., Vo, N. S., & Oviedo-Trespalacios, O. (2022b). Factors influencing intention to use on-demand shared ride-hailing services in Vietnam: Risk, cost or sustainability? Journal of Transport Geography, 99, 103302. https://doi.org/ 10.1016/j.jtrangeo.2022.103302 Nguyen, M. H., & Pojani, D. (2021). Covid-19 need not spell the death of public transport: Learning from Hanoi’s safety measures. Journal of Transport & Health, 23, 101279. https://doi.org/10.1016/j.jth.2021.101279 Nguyen, M. H., & Pojani, D. (2022a). The emergence of recreational cycling in Hanoi during the Covid-19 pandemic. Journal of Transport & Health, 24, 101332. https://doi.org/10.1016/j.jth.2022.101332 Nguyen, M. H., & Pojani, D. (2022b). Why are Hanoi students giving up on bus ridership? Transportation. https://doi.org/10.1007/s11116-021-10262-9 Nguyen, M. H., Pojani, D., Nguyen, T. C., & Ha, T. T. (2021b). The impact of Covid-19 on children’s active travel to school in Vietnam. Journal of Transport Geography, 96, 103191. https://doi.org/10.1016/j.jtrangeo.2021. 103191 Nguyen Thi, B., & Nguyen Thi Thu, H. (2022). Effects of supply chain collaboration on customer loyalty for household electronic appliances in Vietnam. Journal of Asian Business and Economic Studies. Ahead-OfPrint. https://doi.org/10.1108/JABES-11-2021-0189 Nguyen, H. N., Tu, S. S., & Nguyen, M. H. (2020). Evaluating the maiden BRT corridors in Vietnam. Transport and Communications Science Journal, 71(4). https://doi. org/10.25073/tcsj.71.4.3 Nicholas, A. J. (2009). Generational perceptions: workers and consumers. JBER, 7. https://doi.org/10.19030/ jber.v7i10.2344 Ogiemwonyi, O. (2022). Factors influencing generation Y green behaviour on green products in Nigeria: An application of theory of planned behaviour. Environmental and Sustainability Indicators, 13, 100164. https://doi.org/10.1016/j.indic.2021.100164 Pandey, A., & Parmar, J., 2019. Factors affecting consumer’s online shopping buying behavior (SSRN Scholarly Paper No. 3308689). Social Science Research Network, Rochester, NY. https://doi.org/10. 2139/ssrn.3308689 Pappas, I. O., Giannakos, M. N., & Chrissikopoulos, V., 2012. Personalized services in online shopping: Enjoyment and privacy. International Conference on Information Society (i-Society 2012). pp. 168–173. London, UK: IEEE. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64, 12–40. https://www.researchgate.net/ publication/225083802_SERVQUAL_A_multiple-_ Item_Scale_for_measuring_consumer_perceptions_ of_service_quality Perea, Y., Monsuwé, T., Dellaert, B. G. C., & de Ruyter, K. (2004). What drives consumers to shop online? A literature review. International Journal of Service Industry Management, 15(1), 102–121. https://doi. org/10.1108/09564230410523358 Peter, J. P., & Ryan, M. J. (1976). An investigation of perceived risk at the brand level. Journal of Marketing Research, 13(2), 184–188. https://doi.org/10.1177/ 002224377601300210 Ritchie, H., Mathieu, E., Rodés-Guirao, L., Appel, C., Giattino, C., Ortiz-Ospina, E., Hasell, J., Macdonald, B., Beltekian, D., & Roser, M. (2020). Coronavirus pandemic (COVID-19). Our World in Data. https://our worldindata.org/covid-vaccinations Sarstedt, M., Henseler, J., & Ringle, C. M. (2011) Multigroup analysis in Partial Least Squares (PLS) path modeling: alternative methods and empirical results. In M. Sarstedt, M. Schwaiger, & C. R. Taylor (Eds.), Measurement and research methods in international marketing, advances in international marketing (pp. 195–218). Emerald Group Publishing Limited. https:// doi.org/10.1108/S1474-7979(2011)0000022012 Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017) Partial least squares structural equation modeling. In Handbook of market research C. Homburg, M. Klarmann, & A. Vomberg Eds. Springer International Publishing 1–40. https://doi.org/10.1007/978-3-319-05542-8_ 15-1 Saut, M., & Saing, T. (2021). Factors affecting consumer purchase intention towards environmentally friendly products: A case of generation Z studying at universities in Phnom Penh. SN Bus Econ, 1(6), 83. https:// doi.org/10.1007/s43546-021-00085-2 Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90–103. https://doi.org/10.1016/ j.im.2006.10.007 Schuitema, G., Anable, J., Skippon, S., & Kinnear, N. (2013). The role of instrumental, hedonic and symbolic attributes in the intention to adopt electric vehicles. Transportation Research Part A: Policy and Practice, Psychology of Sustainable Travel Behavior, 48, 39–49. https://doi.org/10.1016/j.tra.2012.10.004 Seiders, K., Voss, G. B., Grewal, D., & Godfrey, A. L. (2005). Do satisfied customers buy more? Examining moderating influences in a retailing context. Journal of Marketing, 69(4), 26–43. https://doi.org/10.1509/ jmkg.2005.69.4.26 Shah, S. K., Zhongjun, T., Sattar, A., & XinHao, Z. (2021). Consumer’s intention to purchase 5G: Do environmental awareness, environmental knowledge and health consciousness attitude matter? Technology in Society, 65, 101563. https://doi.org/10.1016/j.tech soc.2021.101563 Shao, R., Derudder, B., & Witlox, F. (2022). The geography of e-shopping in China: On the role of physical and virtual accessibility. Journal of Retailing and Consumer Services, 64, 102753. https://doi.org/10. 1016/j.jretconser.2021.102753 Shukla, M., Jain, V., & Misra, R. (2021). Factors influencing smartphone based online shopping: An empirical study of young Women shoppers. Asia Pacific Journal of Marketing and Logistics, 34(5), 1060–1077. https:// doi.org/10.1108/APJML-01-2021-0042 Siikavirta, H., Punakivi, M., Kärkkäinen, M., & Linnanen, L. (2008). Effects of E-commerce on greenhouse gas emissions: A case study of grocery home delivery in Finland. Journal of Industrial Ecology, 6(2), 83–97. https://doi.org/10.1162/108819802763471807 Spreng, R. A., Harrell, G. D., & Mackoy, R. D. (1995). Service recovery: Impact on satisfaction and intentions. Journal of Services Marketing, 9(1), 15–23. https://doi. org/10.1108/08876049510079853 Statista, 2021. Vietnam: Population density of Ha Noi 2020 [WWW Document]. Statista. URL https://www.sta tista.com/statistics/1188695/vietnam-hanoipopulation-density/ (accessed 5.4.22) Statista, 2022a. Global retail e-commerce market size 2014-2023 [WWW Document]. Statista. URL https:// www.statista.com/statistics/379046/worldwide-retail -e-commerce-sales/ (accessed 5.3.22) Nguyen Thi et al., Cogent Business & Management (2022), 9: 2143016 https://doi.org/10.1080/23311975.2022.2143016 Page 23 of 25