Switching intention to online channel in Vietnam – A case study of consumer electronics goods

Hung, Phan Duy; Thong, Vu Huy; Tuan, Pham Van; Khoa, Nguyen Huu Dang; Trang, Nguyen Quynh Article Switching intention to online channel in Vietnam – A case study of consumer electronics goods Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Hung, Phan Duy; Thong, Vu Huy; Tuan, Pham Van; Khoa, Nguyen Huu Dang; Trang, Nguyen Quynh (2024) : Switching intention to online channel in Vietnam – A case study of consumer electronics goods, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 11, Iss. 1, pp. 1-26, https://doi.org/10.1080/23311975.2023.2291861 This Version is available at: https://hdl.handle.net/10419/325913 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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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/ Cogent Business & Management ISSN: 2331-1975 (Online) Journal homepage: www.tandfonline.com/journals/oabm20 Switching intention to online channel in Vietnam – A case study of consumer electronics goods Phan Duy Hung, Vu Huy Thong, Pham Van Tuan, Nguyen Huu Dang Khoa & Nguyen Quynh Trang To cite this article: Phan Duy Hung, Vu Huy Thong, Pham Van Tuan, Nguyen Huu Dang Khoa & Nguyen Quynh Trang (2024) Switching intention to online channel in Vietnam – A case study of consumer electronics goods, Cogent Business & Management, 11:1, 2291861, DOI: 10.1080/23311975.2023.2291861 To link to this article: https://doi.org/10.1080/23311975.2023.2291861 © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. View supplementary material Published online: 21 Apr 2024. Submit your article to this journal Article views: 1893 View related articles View Crossmark data Citing articles: 2 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oabm20 MARKETING | REsEARch ARTIclE Cogent Business & ManageMent 2024, VoL. 11, no. 1, 2291861 Switching intention to online channel in Vietnam – A case study of consumer electronics goods Phan Duy hunga, Vu huy Thongb, Pham Van Tuanb, Nguyen huu Dang Khoab and Nguyen Quynh Trangb aFaculty of economics and Management, electric Power university, Hanoi, Vietnam; bFaculty of Marketing, national economics university, Hanoi, Vietnam ABSTRACT Online retailing has been growing significantly. however, electronic retailers have still encountered some issues related to consumer psychology and potential risks besides the widely acknowledged advantages of the online channels. This current study extends the PPM (Push-Pull-Mooring) framework by adding customer psychological variables to explain the consumers’ switching intention to online channels. In detail, Push refers to limitations of traditional channel at physical stores, Pull and Mooring present pros and cons of online channel. The relationship of consumer psychology, including Perceived Risk, Attitude towards switching and habit of buying goods at physical stores were integrated to adapt to the characteristics of buying consumer electronics goods. conducting a survey of 1104 customers grouped in 3 big cities: hanoi, ho chi Minh city, and Da Nang, the research results show that: consumer skepticism originating from Perceived Risk negatively influences Attitude towards switching (−0.192); Attitude towards switching channels has the strongest influence (0.374) on switching intention, while buying habits at traditional stores lessens the switching intention (−0.136). This is the new contribution to existing literature regarding switching from traditional channels to online channels for goods requiring consumer experiences. 1. Introduction Online retailing is booming both globally and in emerging markets like Vietnam. Despite the challenges posed by the cOVID-19 pandemic, the online retail sector saw a remarkable growth of 15% ($13.2 billion) in 2021 (VEcOM, 2020), with estimates projecting a growth rate of 29% from 2020 to 2025, resulting in a market size of $52 billion by 2025 (Anh, 2021). According to websolution (2019), electronics items are also among the top 10 best-selling items online. The figures of prestigious organizations for the years 2019, 2020, 2021 also clearly show this (AI, 2020; Reputa, 2021; VEcOM, 2020).The pandemic and the “new normal” have led to a surge in online purchases, particularly among consumer electronics like laptops, desktops, and accessories for various purposes such as work, learning, and entertainment (Khải, 2021; VEcOM, 2022). however, it is essential to note that the transition from physical stores to online shopping is not complete, especially for high-priced or experiential products (Nielsen, 2017). concerns about product quality, lack of accreditation, and the absence of the physical store experience remain significant factors hindering the shift to online channels (VEcOM, 2022). As far as we could reach, there are only 05 studies on switching intention of from offline to online channel, and moreover, these studies are different contents: intention to switch English learning from offline to online (chen & Keng, 2019), intention to switch purchases of general items from a physical © 2023 the author(s). Published by informa uK Limited, trading as taylor & Francis group. CONTACT Vu Huy thong [email protected] Faculty of Marketing, national economics university, no. 207 giai Phong Road, Hai Ba trung District, Hanoi City, Vietnam supplemental data for this article is available online at https://doi.org/10.1080/23311975.2023.2291861. https://doi.org/10.1080/23311975.2023.2291861 this is an open access article distributed under the terms of the Creative Commons attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. the terms on which this article has been published allow the posting of the accepted Manuscript in a repository by the author(s) or with their consent. ARTICLE HISTORY Received 17 April 2023 Accepted 01 December 2023 KEYWORDS buying offline and online; PPM; switching intention REVIEWING EDITOR Kaouther Kooli, Bournemouth University, United Kingdom SUBJECTS Asian studies; consumer Psychology; Business, Management and Accounting 2 P. DUY hUNG ET Al. store to a mobile device (chang et al., 2017), intention to switch to using cloud-based healthcare services (lai & Wang, 2015), intention to switch to online grocery shopping (Monoarfa et al., 2023) and switching behavior of buying cosmetics online thanks to AR technology (Nugroho & Wang, 2023). studies on this channel switching theme are very rare, as mentioned in Nimako (2012). There is a lack of research into a systematic model of significant factors that represent the influences of advantages and limitations of buying in online channel and buying at physical stores on the intention of switching buying channel. Besides, for products that require customer experience like consumer electronics goods, psychological hindrance may make customers more hesitant in their intention to switch to buying on an online channel. In order to fill in these two research gaps, this research aims at investigating factors affecting the switching intention, which remained quite rare in literature (chen & Keng, 2019; lai & Wang, 2015), from the perspective of the PPM framework. By review studies that contains factors suitable for the essential meaning of each “Push”, “Pull”, “Mooring” group, we build up the model based on the PPM paradigm as follows: + Push is the limitations of buying in physical stores: Forsythe et al. (2006), lester et al. (2006), chiu et al. (2011) … + Pull is the advantages of buying online: Forsythe et al. (2006), lester et al. (2006), Jiang et al. (2013), Ye and Potter (2011a), Monoarfa et al. (2023)) … + Mooring is the limitations of buying online: Forsythe et al. (2006), lester et al. (2006), Nugroho and Wang (2023), Ye and Potter (2011a), Bustamante and Rubio (2017), … Among those factors is the Perceived risk factor of buying electronic goods online as an antecedent for customers’ hesitating psychology. + such psychological factors as attitude (A) (Pookulangara et al., 2011; Primabudi & samopa, 2017; Wani & Malik, 2013a) and habit (h) (limayem et al., 2007) are integrated into the model reflecting customer’s apprehension of potential risks. The linking of perceived risk—attitude – habit—switching intention all have scientific basis. This psychological sequence shows that customers’ reluctance to switch to online shopping for electronic products is due to the awareness of risks that are inherent to online shopping and the habit of buying at physical stores is still somewhat maintained. It is likely that this assumption hinders switching intention, even though buying online has many advantages and is increasingly popular in the digital transformation trend. This is also a research gap on customer psychology, thus we propose a PPM-h-A variant model to examine the influence of consumer psychology on the intention of switching to online buying channels in the context of experience goods. 2. Literature review and hypothesis development 2.1. Switching intention A relevant question that confronts business organizations is how to retain consumers. however, more and more offline platform operators now face customer switching. switching intention is defined as “the possibility of the behavior of switching the transaction of consumers from a previous brand to another competitive brand” (Nimako, 2012). From perspective of buying channel switching, the intention to switch channels presents the customers’ tendency to switch from the current channel to another one, i.e., from offline channels to online ones (chang et al., 2017; chen & Keng, 2019; Monoarfa et al., 2023). customers may switch channels when they believe online channels might be more useful and popular than offline ones (Nugroho & Wang, 2023). The ability to adopt technology, i.e., traceability or payment, increases the intention to switch (Monoarfa et al., 2023; singh & Rosengren, 2020). In addition, customers might feel overloaded and fatigued after buying from offline channels for a long time, which motivates them to stop using the current channels and decide to switch (chaouali et al., 2016; Maier et al., 2015). 2.2. The PPM theory The theory of Push-Pull-Mooring (PPM) was founded by lee (1966) and Moon (1995), and was empirically tested in numerous scholar works (Bansal et al., 2005; haridasan et al., 2021; Monoarfa et al., 2023; Nugroho & Wang, 2023). The PPM theory is applied to represent the linear relationship between a cOGENT BUsINEss & MANAGEMENT 3 combination of factors that influence the switching intention/behavior of human being with regards to accommodation, suppliers, or transaction modes. The PPM theory mainly examines factors that can influence the switching intention including push, pull and mooring. Push factors tend to motivate and push people away from current choices; Pull factors refer to advantages of the new method which may encourage people to switch to the new mode; and Mooring effects refer to psychological and social hindrances, conditions, and limitations of the new method when people consider switching. The Figure 1 below is the framework model: 2.2.1. Push factors consumers were found to encounter certain disadvantages when buying at traditional stores (Aryani et al., 2021; Gupta, 2015). however, these scholars did not empirically evidence the influence of these disadvantages on motivating customers to switch to new channels. There are many different disadvantages such as difficulty in product search, product comparison, waste of time and effort, and even pressures from sellers (Forsythe et al., 2006; Gupta, 2015; lester et al., 2006). These disadvantages may push customers to switch to online channels from traditional channels. chiu et al. (2011) also considers vicarious experience as one of the Push factors that can influence the intention of using free riding cross-channel. When consumers learn that many of their relatives have made successful electronic transactions, they will have an intention to switch. Even when consumers have not done any transaction previously, they can consider the results of others’ transactions as an indirect experience. Thus, the following hypotheses are posited: H1(+): Push factors are positively associated with the switching intention. H1a(+): Disadvantages of traditional channels are positively associated with the switching intention. H1b(+): Vicarious experience of others is positively associated with the switching intention. 2.2.2. Pull factors customers may shop online for a wide variety of reasons such as reasonable price, convenience, perceived ease of use ((handayani et al., 2020; Jiang et al., 2013; lester et al., 2006; Monoarfa et al., 2023; Mpinganjira & studies, 2015). First, scholars (Elida et al., 2019; handayani et al., 2020; hooi et al., 2021; lester et al., 2006) have reported that when buying goods online, customers benefit from a more reasonable price. consumers could easily make price comparisons across online channels. second, buying online is very convenient because buyers can easily search for information; evaluate the product; save time and effort; have the product delivered at selected location, and so forth (Jiang et al., 2013; Mpinganjira & studies, 2015). This factor has been indicated to strongly encourage consumers to switch to buying in online channels (Forsythe et al., 2006; Johan et al., 2020). In addition, perceived ease of use is when a person can use a new technology easily (Davis, 1989; Monoarfa et al., 2023). Users will perceive buying online to be easy when they do not need to make too much effort (Perea et al., 2004). Thanks to current advanced technology, Ye and Potter (2011a) stated that consumers consider switching to online channels because they can use technology on their personal devices. Perceived ease of use has been integrated into Pull factors in the context of buying in Figure 1. the Push-Pull-Mooring framework. source: Bansal et al. (2005) 4 P. DUY hUNG ET Al. online channels (Monoarfa et al., 2023); Bauerová and Braciníková (2021); Kumar and Kashyap (2022)). Thus, the following hypotheses are suggest as follow: H2(+): Pull factors are positively associated with the switching intention H2a(+): Price (in online channels) is positively associated with the switching intention H2b(+): convenience (in online channels) is positively associated with the switching intention H2c(+): Perceived ease of use is positively associated with the switching intention 2.2.3. Mooring factors In the context of switching to online channels, mooring factors are referred to as limitations of buying in online channels, which are also partial advantages of buying in traditional channels at physical stores (Forsythe et al., 2006; han & Kim, 2017; Ilhamalimy & Ali, 2021; lester et al., 2006; Neslin et al., 2006; Nugroho & Wang, 2023; Verhoef et al., 2007; Wang et al., 2013). First, perceived risks in online channels are considered as the biggest challenge. It is defined as the consumers’ uncertainty of experience when they cannot predict outcomes of their purchase decision (chellappa et al., 2005). For instance, consumers may hesitate to buy online if they perceive risks related to information security and privacy. consumers are also concerned about the risk of delivery. Risks might also occur when products cannot be tested prior to use. customers can be irritated when post-purchase benefit does not live up to their initial expectation (Yang et al., 2010). In general, when consumers perceive high possibility of these risks, they hesitate to switch (Bhatti & Ur Rehman, 2020; Marriott & Williams, 2018). secondly, subjective norm is another mooring factor that might be negatively associated with the switching intention (lian & Yen, 2013; Ye & Potter, 2011a). In a study about the psychology of shoppers, prudence and collectivism of oriental people affect online shopping behaviors which might delay their behavior of switching channels (lian & Yen, 2013; Zheng, 2013). Thirdly, experience at physical stores has a significant impact on consumer satisfaction and loyalty (Bascur & Rusu, 2020; Bonfanti et al., 2020; Bustamante & Rubio, 2017; Forsythe et al., 2006; lester et al., 2006; silva et al., 2021; Verhoef et al., 2009). Thus, a lack of physical store experience in online channels might discourage customers from switching channels, thereby being a mooring factor. Therefore, when considering switching to buying in online channels, the lack of in-store experience will discourage customers from switching to online channels. Thus, the following hypotheses can be infered that: H3(-): Mooring factors are negatively associated with the switching intention. H3a(-): Perceived risks (online) are negatively associated with the switching intention. H3b(-): subjective norms are negatively associated with the switching intention. H3c(-): (A lack of) Physical store experience is negatively associated with the switching intention. H3d,3e(-): Mooring factors negatively moderate the impact of Push and Pull factors on the switching intention. 2.2.4. Attitudes towards switching to online channels According to Pookulangara et al. (2011), Attitude toward switching buying channel is defined as consumers’ evaluation of outcomes of their buying behaviors in the channel they choose. scholars have provided different explanations about what leads to attitudes toward switching buying channels (Madahi & sukati, 2014; Primabudi & samopa, 2017; sinha & Kim, 2017; Wani & Malik, 2013b). Results revealed that perceived risks directly impact consumers’ attitude while risks related to finance, products, convenience, and delivery negatively influence attitude towards buying behaviors in online channels. specifically, scholars have found that buying consumer electronics goods in online channels certainly poses a variety of perceived risks for consumers (harn et al., 2006; levin et al., 2003; Zhang, 2008). Therefore, the following hypothesis is: H4(-): Perceived risks (in online channels) are negatively associated with attitude towards switching to online channels. People tend to behave consistently with their Attitude. Bansal and Taylor (1999) state that the more favorable Attitude toward switching, the more likely the switching intention. Findings of Madahi and cOGENT BUsINEss & MANAGEMENT 5 sukati (2014), Nikseresht (2016) and Palau-saumell et al. (2021) also infer that Attitude towards switching positively influences the switching intention to buying in online channel. Therefore, the following hypothesis is: H5(+): Attitude towards switching to online channels is positively associated with the switching intention. 2.2.5. Habit habit has been integrated to the PPM theory to develop the combined framework named “PPM-h” to test the impact of habit on the switching intention in some contexts such as cloud application services; and learning English online with real people (chen & Keng, 2019; cheng et al., 2019; lai & Wang, 2015). Results indicate that individuals unconsciously perform a certain behavior because of repeated activities. Thus, habit can reduce the chance that consumers will consider alternatives; and therefore, stay with the current choices (limayem et al., 2007). This will slightly influence the switching intention (lai & Wang, 2015; sun et al., 2017). Other studies have found out that habit has a negative impact on the switching intention, especially in contexts related to technology (chen & Keng, 2019; cheng et al., 2019; lin et al., 2021). Therefore, the following hypothesis is: H6(-): habit is negatively associated with switching intention. According to Tuu (2015b), habit is indicated to negatively moderate the relationship between attitude and intention because the automaticity of behavior diminishes the need to access to consumers’ perception of switching intention. This means among people who have a habit, the predictive power of attitude on intention seems to be weaker. In contrast, for those who do not have a habit of doing things, their attitude may act as a stronger predictor of their intentions. Most studies have found that the attitude— intention relationship is typically weaker when the behavior is habitual than when the behavior is not habitual (De Bruijn et al., 2007; honkanen et al., 2005). Therefore, the following hypothesis is formally posited: H6a(-): habit negatively moderates the impact of attitude towards switching channels on switching intention. The research model is proposed in this Figure 2: Figure 2. Research model. 6 P. DUY hUNG ET Al. 3. Methodology 3.1. Teamwork discussion/interview Measurement scales are adapted from previous literature (see Index 4). The initial measurement scales consisted of 68 items. After consulting with 04 academic experts in marketing and e-commerce in October 2020, 54 items were finalized to assure the simplicity, the meaningfulness, and the feasibility of the research model. The item scale used is likert 7, with point from 1–7 expressing the assessment of consumers representing strongly disagree to strongly agree. Besides, 11 consumers were interviewed in January 2021 to examine the validity of the survey sheet. This helped revising the questions to make them easy to understand for the respondents. After that, 22/54 items were revised to make them more understandable for Vietnamese people but still maintain their meaningfulness. The second purpose is to explore the main hindrance for customers to switch. Among 3 mooring factors (Perceived risks, subjective norms, lack of Physical store experiences), Perceived risks was voted by 9/11 consumers to be the most concerning issue when they consider switching to online channel. This also suggests Perceived risks as the most influential antecedent variable to set in the hypothesized psychological sequence of Perceived risk—Attitude—habit. Details of the interview records are available upon request. 3.2. Pilot study Due to the complexity of the 2-level structure, we conduct a pilot survey to evaluate the validation of the scale by cFA (confirmatory factor analysis). We distributed survey sheets to 400 participants, collected 312 responses, and filtered out (missing information, answered “vertically”) for 289 valid observations. Regarding sample characteristics: 53.63% male (155) and 46.37% female (134); 34.36% students (99) and 65.64% working (190); the number of online purchases of electronic goods in the last 2 years is 10.55% for 0 times (30), 50.65% for 1–2 times (147), 20.35% for 3–4 times (59) and 18.45% for more than 5 times (53) respectively. The rules for evaluating the quality of the scale are guided in churchill (1979) and steenkamp and van Trijp (1991). Use the software sPss 25, AMOs 22 to assess for both second order structures (Push, Pull, Mooring), and first order ones (Attitude, habit, switching intention). Results of cR, AVE for all latent variables are valid measurement (see index 1). 3.3. Official full survey We continue to use the likert 7 scale for items, and nominal/ordinal for demographic questions. Regarding sample size and distribution, in the materials of F hair et al. (2014), apply the 10× rule of Barclay et al. (1995), and statistical power that cohen (1992) proposal should also be considered when choosing sample size. With effect size = 0.2, statistical force = 0.8; indicator number is 54 and latent variable number is 15, the recommended sample size is 530. We decided to distribute the survey sheet to 2000 participants, expecting the number of valid observation to analyze is about 1000 to ensure meaningful results. According to the report on the e-commerce index by locality in the e-commerce market report of VEcOM (2021), we made survey for the 03 cities of ho chi Minh city, hanoi và Da Nang with rankings 1, 2, 3 point (67.63; 55.66; and 19.04 respectively), as well as regional representativeness. Implement geographical stratification to distribute survey sheet in these three cities. The distribution of sheets is distributed proportionally to the percentage of the population of these three cities. Then, with the total number of sheets distributed in each city, re-allocate them correspondingly to the percentage of the population of the each district. The survey was conducted by snowball distribution through acquaintances with wide social network. This is a “non-sensitive” research issue, because respondents are not pressured to answer to follow social desirability, and the opinion of buying online can be generalized to this modern digital society. The acquaintances are chosen by their living place, then they transfer the sheets to cOGENT BUsINEss & MANAGEMENT 7 participants nearby and online. The covid period lasting from 2020 to early 2022, the digital transformation process is widely known, the conditions to access the internet widely … also support this argument. The actual survey period took place from December 2020 to March 2022 due to the complicated stages of the covid epidemic affecting the results of this study. specifically, there are three phases: the covid epidemic is basically controlled through social distancing (from December, 2020 to before 28 January 2021), social quarantine because of serious community spread (from 28 January 2021 to November, 2021), and adapting to new normal conditions as widely vaccinated (from January 2021 to June, 2022). The reason for surveying in these time intervals is to avoid biased results when synthesizing. After distributing sheets to 2000 participants through both direct survey and online survey, the total number of sheets collected is 1182 votes (59.1%). After collected, survey sheets that are missing information, disordered, or “abnormally vertical” ticks are discarded (F hair et al., 2014). The final number of valid observations is 1104. Different demographic characteristics need to ensure a reasonable ratio, avoid bias. Details are presented in the descriptive statistics (Table 1). Table 1. Demographic information. Demographic Characteristics nominal/ordinal scale number Percentage gender Male 559 50.63% Female 545 49.37% age under 23 384 34.78% 23–30 519 47.01% 30–40 126 11.41% over 40 75 6.79% Profession student 348 31.52% Working 756 68.48% Place Hanoi 496 44.93% Da nang 80 7.25% Ho Chi Minh city 528 47.83% online buy times 0 176 15.94% 1–2 451 40.85% 3–4 210 19.02% over 5 267 24.18% Product type smartphone 254 23.01% sound devices 437 39.58% television 14 1.27% Visual devices 25 2.26% appliance 208 18.84% others 166 15.04% Buying channel Pure online 209 18.93% Click-and-mortar 775 70.20% Facebook 120 10.87% expenditure (usD) under 216 584 52.90% 216–432 214 19.38% 432–2160 279 25.27% over 2160 27 2.45% Phase Controlled 405 36.69% Widespread 312 28.26% normal 387 35.05% + Male accounted for 50.63% (559) compared to 49.37% of females. + the percentages of age of the respondents are 34.78%, 47.01%, 11.41%, 6.79% for age groups of under 23, 23–30, 30–40, and above 40, respectively. + Respondents who are students, and those who are having a job account for 31.52% and 68.48%, respectively. + those who live in Hanoi, Da nang and Ho Chi Minh account for 44.93%, 7.25%, and 47.83%, respectively. this is appropriate with population size ratio (8; 1.2; 9 million in these cities, respectively) when comparing these three cities. + times of buying goods in online channel account for 15.96%, 40.85%, 19.03% and 24.18% for 0, 1–2, 3–4, above 5 times, respectively. + the types of products bought in online channel are mobile phones, sound devices, televisions, visual devices, appliances and others (23.01%, 39.58%, 1.27%, 2.21%, 18.84% and 15.04%, respectively). + types of online channel include pure online retailer, brick-and-mortar, Facebook (18.93%, 70.20% and 10.87%). + expenditure of under 216 usD (equal to 5 million VnD), 216–432 2160 usD, 432–2160 usD and above 2160 usD accounts for 52.90%, 19.38%, 25.27% and 2.45%, respectively. + the phases that CoViD-19 was basically controlled (2020–28/01/2021), CoViD-19 was spreading in the community (28/01/2021–01/2022) and the period that people were adapting to new normal conditions in the society (from 01/2022 on) were also surveyed (with responses accounting for 36.69%, 28.26%, 35.05%, respectively. 14 P. DUY hUNG ET Al. Traders can consider to reduce the Risks of information, delivery, product, after-sales services; thereby encouraging the Attitude towards switching. Entrepreneurs can also propose strategies to form new habit of customers when shopping with new technologies such as virtual reality, online interactive sales, online promotion, mobile commerce marketing, meaning to gradually change the old habit. Acknowledgments This research paper is funded by National Economics University, hanoi, Vietnam. Disclosure statement No potential conflict of interest was reported by the author(s). Funding The work was supported by the National Economics University, hanoi, Vietnam . About the authors Phan Duy Hung is a lecturer at Electric Power University, hanoi, Vietnam. he completed his Ph.D in Business Administration from National Economics University, Vietnam. his research interests include e-business, distribution channels, consumer behaviors and AI-application marketing. 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Appendix 1) Pilot study to assess measurement quality Push Item <—latent variable Factor 1 (EFA) Factor 2 (EFA) loading (cFA) cR (>0.7) AVE (>0.5) Dis1<—Disadvantages 0.825 0.724 0.894 0.685 Dis2<—Disadvantages 0.823 0.818 Dis3<—Disadvantages 0.701 0.688 Dis4<—Disadvantages 0.741 0.635 Ve1<—Vicarious experience 0.887 0.908 0.809 0.517 Ve2<—Vicarious experience 0.909 0.919 Ve3<—Vicarious experience 0.875 0.860 Ve4<—Vicarious experience 0.696 0.578 KMo = 0.841, Bartlett = 1218.176 (sig 0.00); total variance = 69.698%, eigen values 2 components Fornell-Larcker Vicarious experience Disadvantages Vicarious experience 0.82806 Disadvantages 0.474 0.71935 cOGENT BUsINEss & MANAGEMENT 21 Pull Item <—latent variable Factor 1 (EFA) Factor 2 (EFA) Factor 3 (EFA) loading (cFA) cR (>0.7) AVE (>0.5) Peou1<—Peou 0.770 0.614 0.839 0.570 Peou2<—Peou 0.779 0.692 Peou3<—Peou 0.782 0.885 Peou4<—Peou 0.790 0.803 Pr1<—Price 0.827 0.768 0.849 0.584 Pr2<—Price 0.700 0.735 Pr3<—Price 0.757 0.762 Pr4<—Price 0.790 0.792 Co1<—Convenience 0.682 0.743 0.912 0.601 Co2<—Convenience 0.691 0.658 Co3<—Convenience 0.760 0.795 Co4<—Convenience 0.735 0.750 Co5<—Convenience 0.776 0.797 Co6<—Convenience 0.772 0.841 Co7<—Convenience 0.830 0.827 KMo = 0.915, Bartlett = 2599.009 (sig 0.00); total variance = 68.212%, eigen values 3 components Standard Fornell-Larcker Price Peou Conveni-ence Price 0.76452 Peou 0.553 0.75563 Convenience 0.626 0.628 0.77515 22 P. DUY hUNG ET Al. Item <—latent variables Factor 1 (EFA) Factor 2 (EFA) Factor 3 (EFA) loading (cFA) cR (>0.7) AVE (>0.5) sn1<—sn 0.815 0.833 0.901 0.504 sn2<—sn 0.783 0.857 sn3<—sn 0.725 0.781 sn4<—sn 0.719 0.640 PRon1<—Perceived risks 0.808 0.781 0.861 0.612 PRon2<—Perceived risks 0.633 0.628 PRon3<—Perceived risks 0.734 0.687 PRon4<—Perceived risks 0.629 0.642 PRon5<—Perceived risks 0.802 0.788 PRon6<—Perceived risks 0.779 0.774 PRon7<—Perceived risks 0.623 0.693 PRon8<—Perceived risks 0.685 0.694 PRon9<—Perceived risks 0.652 0.687 Pse1<—PhysicalstoreeXP 0.637 0.743 0.901 0.566 Pse2<—PhysicalstoreeXP 0.764 0.782 Pse3<—PhysicalstoreeXP 0.694 0.70 Pse4<—PhysicalstoreeXP 0.766 0.763 Pse5<—PhysicalstoreeXP 0.769 0.732 Pse6<—PhysicalstoreeXP 0.759 0.732 Pse7<—PhysicalstoreeXP 0.820 0.812 KMo = 0922, Bartlett = 3438.71 (sig 0.00); total variance = 62.627%, eigen values 3 components Fornell-Larcker Perceived Risks subjective norms Physicalstoreexp Perceived Risks 0.78229 subjective norms 0.606 0.71043 Physicalstoreexp 0.535 0.618 0.75278 cOGENT BUsINEss & MANAGEMENT 23 Item <—latent variable loading (EFA) loading (cFA) cR (>0.7) AVE (>0.5) at1<—attitude 0.842 0.842 0.867 0.62 at2<—attitude 0.817 0.833 at3<—attitude 0.791 0.768 at4<—attitude 0.818 0.702 Ha1<—Habit 0.882 0.850 0.885 0.659 Ha2<—Habit 0.856 0.796 Ha3<—Habit 0.849 0.790 Ha4<—Habit 0.861 0.810 si1<—switching intention 0.827 0.768 0.798 0.569 si2<—switching intention 0.773 0.716 si3<—switching intention 0.825 0.778 2) PLS SEM Validation Perspective Validation thresholds Reflective construct - internal consistency (Cronbach’s alpha, CR) - Convergent validity (aVe) - Discriminant validity (Fornell-Larcker) - Cronbach’s alpha > 0.6; 0.6 < CR < 0.95 - aVe > 0.5 - aVe> correlation coefficient between a latent variable and other Formative construct - Convergent validity (aVe, w) - Colinearity between indicators (ViF) - significant and relevance of outer weight (p-value and hypothesis) - aVe > 0.5, w > 0.8 (path between formative and reflective constructs of the same latent variable) - ViF < 5 - p-value < 0.05 and reasonable with hypothesis inner model (among latent variables) - Colinearity between variables - P regression path coefficient - Determination coefficient - ViF < 5 - P suitable to hypothesis and p-value <0.05 - 0 < R2 < 1 Predictivity - apply the blindfolding and PLspredict - Q2: weak (<0.02), moderate (>0.15 and <0.35), strong (>0.35); RMse(or Mae) of PLs-seM < those of LM 3) Common method bias ViF RandomVar attitudeVar 1.498 ConvenienceVar 2.845 (Continued)