Understanding online shopping adoption: The unified theory of acceptance and the use of technology with perceived risk in millennial consumers context

Online shopping is growing so rapidly and has attracted millennials in various way. Unfortunately, the discussion regarding the adoption of online shopping in millennial consumers’ context with perceived risk application was still limited. Therefore, the purpose of this study was to investigate the effect of performance expectancy, expectation efforts, social influence, facilitation conditions, hedonic motivation, price value, habits, and perceived risks on behavioral intentions and use behavior. This study also discusses the effect of perceived risks on financial risk, performance risk, and privacy risk. This study can be classified as explanatory research with purposive sampling and partial least square as sampling techniques and data analysis. This study was designed to focus on individuals who can be classified as an online shopper with a range of age of 18-35 years old. The results show that the millennial generation is influenced by the social environment and habits in shaping their behavioral intention. Millennial consumers are also proving very concerned about their perceived risk of financial, performance, and privacy issues when doing online shopping. Interestingly, six of the factors studied (performance expectancy, effort expectancy, facilitating conditions, hedonic motivation, price value, and perceived risk) do not have any influence on the intention to use online commerce technology.


Introduction
The availability of the internet on computers and mobile devices is increasing the growth of e-commerce markets throughout the world. Consumers can more easily access various products from several vendors at any time by shopping online (Hurtado et al., 2019). Today's internet users in Indonesia are mostly carried out by millennials. Millennial consumers are an important issue in the development of the digital world and social media is a communication space for this generation (Khechine et al., 2020). This condition underlies the selection of millennial consumers as the object of this research in adopting e-commerce using the Unified Theory of Acceptance method and Use of Technology in Consumer Context (UTAUT2).
UTAUT2 is the development of the UTAUT model concept (Venkatesh et al., 2012).
UTAUT2 illustrates how consumers adapt to technology. Millennial consumers also concerned about risk perception in deciding whether they should purchase online or not (Wang & Herrando, 2019). Lestari (2019) and Amrullah & Priyono (2018) even added that perceived risks have a positive effect on attitudes and intentions to adopt e-commerce.
Previous research has provided limited understanding regarding the adoption of online shopping in millennial consumers' context with perceived risk application. Therefore, the purpose of this study was to investigate the effect of performance expectancy, expectation efforts, social influence, facilitation conditions, hedonic motivation, price value, habits, and perceived risks on behavioral intentions and use behavior. This study also discusses the effect of perceived risks on financial risk, performance risk, and privacy risk.

Unified Theory of Acceptance and Use of Technology (UTAUT)
UTAUT has been widely used models to predict behavioral intention. Celik (2016) stated that the UTAUT model proved a robust and valid model in the online shopping context which means that the model can be used to explain the degree of purchase intention and actual purchase. Cimperman et al. (2016) defined UTAUT as "a measure of the strength of one's intention to perform a specified behavior." The UTAUT model argues that an individual adoption on new technology mainly influenced by four factors which is performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC). Most studied about UTAUT was focus only on a subset of the model (Venkatesh et al., 2012) and not have utilized moderators like age, experience, and gender because it may not be any variation in the moderator for the adoption (Dwivedi et al., 2019). belief that using technology (internet) will help him or her to attain gains in their shopping activities. Consumers perceived that using the internet for shopping will provide benefits and help them saving time, money, effort, and gain service effectiveness (Venkatesh et al., 2012).
Effort expectancy (EE) defined as the level of convenience associated with internet usage (Liu et al., 2018). It means that if consumers found that the use of the internet for online shopping is easy and does not require any effort, the possibility of online shopping adoption will be higher. Previous studies from Venkatesh et al. (2012), Kabra et al. (2017), Jewer (2018 found that there is a significant relationship between performance expectancy and effort expectancy on behavioral intention. Therefore, the proposed hypotheses in this study can be described as follow. can encourage consumers to adopt the use of the internet for online shopping. (Venkatesh et al., 2003) has been defined facilitating conditions (FC) as "the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system" including knowledge, capabilities, and consumer resources (Venkatesh et al., 2012).
If internet infrastructure and knowledge that needed in using the internet available, also there is support from social for using the internet, the intended behavior to adopt the internet for online shopping will be increased. Previous studies from Venkatesh et al. (2003), Diño & de Guzman (2015) concluded that there is a significant relationship between social influences and facilitating conditions on behavioral intention. Facilitating conditions also prove significantly affects actual usage behavior (Zuiderwijk et al., 2015). Behavioral intention (BI) reflects the extent to which customers are likely to use technology (Venkatesh et al., 2012). Behavior intention is the most powerful determining factor in individual behavior towards technology acceptance (Alalwan et al., 2018).
Understanding online shopping adoption: The unified theory of acceptance and the use of technology in millennial consumers context and perceived risk application by Rian Piarna, Ferdi Fathurohman, Nunu Nugraha 54 H5: Behavioral intention (BI) significantly affect use behavior (UB) Venkatesh et al. (2012) and Tak & Panwar (2017) stated that to be more adaptive to consumer use framework, the original UTAUT model has been extended to UTAUT 2 which added three new constructs namely hedonic motivation, price value, and habit. While hedonic motivation (HM) defined as s the "fun or pleasure derived from using a technology" by (Venkatesh et al., 2012). Price value (PV) and habit (H) can be defined as "consumers'

Hedonic Motivation, Price Value, and Habit
cognitive tradeoff between the perceived benefits of the applications and the monetary cost for using them" and s "the extent to which people tend to perform behaviors automatically because of learning" (Herrero et al., 2017). Previous studies from (Alalwan et al., 2017), (Herrero et al., 2017), and (Wong et al., 2020) found that behavioral intention was significantly influenced by hedonic motivation, price value, and habit. Not only influences behavioral intentions, (Gupta & Dogra, 2017) also found the significant contribution of habit on actual usage behavior. risk plays an important role as an inhibitor online purchase decision. A study from (Indiani et al., 2015) and (Zulfikar & Mayvita, 2018) even concluded that perceived risk has a stronger impact on an actual purchase than trust. Perceived risk itself can be divided into three categories namely financial risk, performance risk, and privacy risk. While financial risk (FR) is a potential monetary loss that will be experienced by consumers when shopping online (Salam et al., 2003). Han & Kim (2017) defined performance risk (PFMR) as the risk caused by electronic services (shopping websites) whose performance does not meet consumer expectations. Warkentin et al. (2002) added that privacy risk (PVR) can be defined as the expected losses control of their personal information that caused by their online shopping activities or identity theft (Han & Kim, 2017). These three risks that are felt by consumers are second-order factors that influence the intention to use the internet to shop online. Thus, the lower degree of perceived risk will impact the higher possibility of the adoption of online shopping (Choi et al., 2013;Tingchi Liu et al., 2013).
H9a: There is a significant influence of perceived risk (PR) on financial risk (FR) H9b: There is a significant influence of perceived risk (PR) on performance risk (PFMR) H9c: There is a significant influence of perceived risk (PR) on privacy risk (PVR) H10: There is a significant influence of perceived risk (PR) on behavioral intention (BI)

Figure 1. Research Framework
This study can be classified as explanatory research. Purposive sampling and partial least square used as sampling techniques and data analysis. This study was designed to focus on individuals who can be classified as an online shopper with a range of age of 18-35 years old.
The rules for taking a minimum number of samples in PLS-SEM are two conditions; the first is based on ten times the number of formative indicators and secondly, it is based on ten Understanding online shopping adoption: The unified theory of acceptance and the use of technology in millennial consumers context and perceived risk application by Rian Piarna, Ferdi Fathurohman, Nunu Nugraha 56 times the number of lines (paths) connecting each latent variable in the study (Helm, 2005).
Therefore, the number of collected samples in this study were 150 samples.  significantly proved to affect perceived risk in this study. Our study revealed that respondents in this study were largely influenced by the endorsement of their peer group (family and friend) in deciding whether they should try online shopping or not. They also have all technology and human resources (like mobile devices, internet, know-how to utilize the internet, also supportive peer groups) which make them more confident to make an actual purchase (not only limited on behavioral intention) through the online channel. The majority of respondents in this study also can be classified as tech-savvy, a technology geek that familiar with the use of the internet and always up-to-date with the technology they use (Narasuman et al., 2011). Therefore, as the usage of the internet become a habit on the millennial generation, it appears that habit will automatically guide future behavior in using the internet to purchase products. Ouellette & Wood (1998)  had no significant effect on behavioral intention. Nowadays, millennial believes that the internet is easy to access and provide a better experience. Thus, completing an online transaction is no longer becoming an issue in online commerce (Yu & Chen, 2018). As a result of that, performance expectancy, effort expectancy, and facilitating conditions is no longer a determining factor that would influence behavioral intention as it used to.
This study also concluded that the adoption and the use of technology (online commerce) were not influenced by hedonic motivation and price value. Pham et al. (2018) andKapferer et al. (2014) argue that an individual with hedonic personality will tend to see the pleasure, joyfulness, luxurious, and exclusive experiences that brand offers first. They are not looking for a new technology of online shopping that affordable only, they are looking for technology that can give them pleasures, exclusivity, and enhances their satisfaction. Octaviani & consumers feel does not affect the intention of consumers to buy a product online which means that perceived risk had no significant effect on behavioral intention. (Primanto et al. (2018) argues that the good reputation of the online marketplace is enough to make consumer believes in making a purchase decision. Therefore, consumers perceived risk will be lower when the marketplace vendor is more well-know.

Conclusion and Suggestion
The results of this study indicate that the millennial generation is influenced by the social environment and habits in shaping their behavioral intention. It means that respondents in this study were largely influenced by the endorsement of their peer group (family and friend) and as the usage of the internet becomes a habit on the millennial generation, it appears that habit will automatically guide future behavior in using the internet to purchase products. Millennial consumers are also proving very concerned about their perceived risk of financial, performance, and privacy issues when doing online shopping. They also believe that the internet is easy to access and provide a better experience. Therefore, performance expectancy, effort expectancy, and facilitating conditions are no longer a determining factor that would influence behavioral intention as it used to. Regarding the absence effect of hedonic motivation, price value, perceived risk on the adoption and the use of online commerce technology it indicates that they are looking for online commerce technology that can give them measurement, exclusivity, and enhances their satisfaction not just affordable.
They also believe that the well-known reputation of an online marketplace is enough to make them purchasing through online channels.