JSA 2Volume 4, No. 12, Desember 2023

p-ISSN 2722-7782 | e-ISSN 2722-5356

DOI: https://doi.org/10.46799/jsa.v4i7.671

 

THE INTENTION TO USE E-COMMERCE VS. PHYSICAL SHOPPING IN THE POST-COVID POLICY IN INDONESIA

 

Amelya Pane-Citra1, Suwanto Ade2, Eliahannanti Firdha3, Harianto V. Monica4

1,2,3,4 Universitas Prasetiya Mulya Indonesia

Email:

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Abstract

Customers' shopping behavior since the Covid-19 pandemic have changed due to post-covid social restriction in Indonesia. This research study behavior patterns to find out consumer shopping trends in Indonesia after Post-Covid policy as a reference for market players to grow business in online or offline stores based on drivers (UTAUT 2, herd behavior) dan barriers perspectives. The sample in this study was 487 respondents from the population of Indonesia, and sampling technique used in this research is Probability Sampling. The data was carried out by collecting questionnaires containing 51 items related to the variables in this study. The results show that Performance Expectancy, Habit, Imitating Others have a positive effect on E-commerce Intentions Variables and the Technology Fear factors has a negative effect towards Ecommerce Intention. In addition, the Privacy Risk Variable have a positive impact on the Physical Shop Intention Variable, while hedonic motivation factor has a negative impact on Physical Shop Intention. The result would have contribution for business owners to have more understanding on Indonesian market in order to develop business in e-commerce and physical stores.

 

Keywords: consumer behavior, e-commerce intention, physical shop intention

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INTRODUCTION

The Covid-19 crisis is one of the most devastating recent tragedies. Due of its significant consequences, numerous research in a range of sectors, including consumer behavior, have been interested in it. It has huge social and economic effects on the entire world. The World Health Organization (WHO) has noted that COVID-19 is responsible for almost four million fatalities globally (WHO, 2021). On top of that, economies have suffered significant damage (Donthu and Gustafsson, 2020). The International Monetary Fund (IMF) predicts that by 2020, the global gross domestic product will have decreased to 4.9%. (IMF, 2020). The pandemic's enormous social and economic effects and particular features have served as inspiration for many studies in a range of sectors, including consumer behavior. Due to the crisis, consumer behavior has dramatically changed in favor of a technological one (Sheth, 2020).

The COVID-19 epidemic has significantly affected consumer and corporate activity. While the influence of COVID-19 on enterprises' digital transformation has been researched, relatively less scholarly attention has been paid to how consumers and consumer behavior are affected (Kim, 2020; Verma and Gustafsson, 2020). Consumer surveys and comments from the industry indicate that the epidemic has increased a tendency toward e-commerce that was seen before the crisis (Kim, 2020).

Consumer anxiety over the epidemic has had a huge impact on how customers perceive the financial and environmental benefits of e-commerce platforms (Tran, 2021). During the pandemic, the digitalization of the market and the habits has been formed (Kim, 2020; Sheth, 2020). It may cause structural changes to consumption as people continue to display their modified behaviors after the pandemic has ended, similar to those observed in China in 2002�2003 during the SARS pandemic (Clark, 2018).

When the pandemic first struck Indonesia, the Indonesian government, in accordance with a statement during a press release by President Jokowi, announced the social restriction policy as a restriction towards community activities (kemlu.go.id). The social restriction policy itself was repealed December 30, 2022 (covid19.go.id)

More than 15,000 online retailers in Indonesia were surveyed in December 2020 by the World Bank and Shopee. In the survey, participants were asked how the pandemic affected their business operations and performance, how they handled the epidemic, and what kind of government and business help they had received or desired. The data discovered that internet merchants in the COVID-19 era outbreak than those that run mostly offline operations, in accordance with Hammouri (2021) shows that people are increasingly avoiding physical shop purchases and shifting to digital Shop. Indonesia e-commerce data from Bain & Company shows that e-commerce CAGR increases 39% from 2019 to 2021.

But even while internet sales (online shopping) have increased globally and in Indonesia particularly since the epidemic began, it is still unclear what factors influence this behavior. Further study is required to comprehend the factors that influence and the obstacles that impede the adoption of electronic commerce or the intention to use it, and how online consumption is changing as a result of the pandemic, and the potential significance of electronic commerce in the post-COVID-19 world (Barnes,2020, O'Leary,2020, Peji-c-Bach,2020).

The UTAUT model has been used and tested extensively since its inception for anticipating system utilization and making decisions related to the adoption and use of technology in a variety of sectors, such as interactive whiteboards (Umak et al., 2017). UTAUT2 is the extended version of UTAUT model. The UTAUT2 model has been used in several prior research to examine variables (Acharya et al., 2019) that affect user�s intentions to use technological products, including mobile commerce, e-payment, and mobile marketing (Eneizan et al., 2019). Reference (Shaw and Sergueeva, 2019) states that some factors like performance expectancy, privacy risk, perceived value aspects and hedonic motivation behavior were factors that affected user�s intentions to use mobile commerce.

By including additional relevant variables studied in the scientific literature into the UTAUT2 model, this study attempts to assess the factors that influence and hinder the adoption of e-commerce. Retailers may now foresee and get around drivers� factors to e-commerce adoption by considering factors including performance expectancy, effort expectancy, social influence, habit, facilitating conditions, hedonic motivation, and trust. Those are believed as the drivers of e-commerce intention that later will be discussed in this study. Determining the barriers that consumers consider when opting to use internet commerce to satisfy their demands is crucial. This study will adopt technophobia or technology fear, perceived risk, switch cost, and privacy risk concerns as the main barriers.

In this regard, recent studies have proposed various reviews of the UTAUT2 model's significance (Taneja & Bharti, 2021, Tamilmani et al., 2021), said that one of the models that the scientific literature utilizes most frequently is one that examines how technology is adopted as well as how to incorporate new factors to enhance its predictive power (Yawised et al., 2022; Kuriakose et al., 2022, Migliore et al., 2022). UTAUT2 is one of the models that is most frequently used to study the innovation adoption process is and its later extensions, according to a recent definition in the scientific literature (Tamilmani et al., 2020). Compared to previous technological acceptance models, Singh et al. (2020) state that UTAUT2 is more suitable for this study because it provides a better description of the numerous constructs used to measure behavioral intentions.

Potential user�s decision-making processes have been impacted by the COVID-19 pandemic in such a way that they now frequently rely on information sources outside of their immediate social circles, as well as how the information is obtained and observations of other people's behavior, rather than relying solely on firsthand experience. This is consistent with the way that herd behavior and social influence are different from one another (Sun, 2013), and it offers a chance to examine UTAUT in the context of herd behavior.

Herd behavior was found to have a strong bearing on behavioral intention (Erjavec and Manfreda, 2022). Herd behavior as a phenomenon has already been proposed to be employed in studies connected to information management research or as a new area of research in relation to UTAUT (Popovic and Trkman, 2016, Kim and Hall, 2020). Based on that, this study tries to combine the UTAUT2 Model and herd behavior, as herd behavior is a factor that is related to the behavioral intention, or in this study specified as the e-commerce intention. On brief, it can be said that this study tries to analyze the drivers and barriers of e-commerce adoption using the UTAUT2 Model in the context of herd behavior.

 

RQ1: Analyze the profile of Indonesian users after Post-Covid policy regarding e-commerce based on technology adoption theory of UTAUT 2, extended barriers and herd behavior variable

RQ2: Analyze the behavior of Indonesian users regarding traditional commerce from the perspective of the barriers that these users display regarding e-commerce.

2. Literature Review and Research Hypotheses

The UTAUT 2 model's literature will be reviewed in this section to understand the customer behavior that build the e-commerce intention or adoption. Following with the discussion about the seven key elements in UTAUT2 model which are the drivers of ecommerce intention for e-commerce adoption and finally discussed about the herd behavior in consumer behavior that affects the behavioral intention of customer.

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2.1. Drivers to E-commmerce: Extentions of UTAUT 2 model in E-commece

Numerous reviews over the past few years have utilized various technology adoption models (Patil et al., 2020, Sanchez et al., 2021). Based on a thorough analysis and comparison of the aforementioned models, Venkatesh et al. (2003) proposed an integrated model known as the UTAUT model that can account for 70% of the variance in user intention. The UTAUT model is the best model for analyzing technological acceptance, according to the conclusions of that empirical study. Theory of Reasoned Action (TRA), Technology Acceptance Model (TAM), Motivational Model (MM), Theory of Planned Behavior (TPB), Combined TAM and TPB (C-TAM-TPB), Model of PC Utilization (MPCU), Innovation Diffusion Theory (IDT), and Social Cognitive Theory make up this model, which combines eight theories of information technology acceptance.

�UTAUT considers factors including performance expectancy, effort expectancy, social influence, other circumstances that facilitate acceptance, the role of gender and age, experience, and use. The six key elements of the UTAUT model are performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC) to utilize the system, and usage behavior.

Whilst UTAUT2 was the extended version of UTAUT, it added three additional constructs which are hedonic motivation, pricing value, and habit and placed more emphasis on the hedonic value (intrinsic motivation) of users toward technology (Venkatesh et al., 2012). UTAUT2 model, the evolution of UTAUT, studies how technology is embraced and applied in the context of the user. In addition, age, gender, and experience are employed as moderating variables in UTAUT2 to influence behavioral intentions when utilizing technology.

 

2.1.1. Performance Expectancy (PE)

Performance Expectancy (PE) is defined as the degree to which a person is confident that product�s technology will benefit them in carrying out specific tasks (Venkatesh et al., 2003) or described as a prospective motive of behavioral intentions. Some of them examine whether consumers accept mobile marketing (Acharya et al., 2019). and an investigation into consumers' acceptance of smartphone diet apps (Okumus et al., 2018). It is important to acknowledge that Performance Expectancy (PE) influences a person's decision to employ a technology product. Based on the above, the following hypothesis is developed:

H1. Performance Expectancy (PE) has a positive impact on E-commerce intention to use.

2.1.2. Effort Expectancy (EE)

A technology product's effort expectancy (EE) is a gauge of how easy it is to use (Venkatesh et al., 2003) or known as perceived ease of use to explain the adoption of computer systems (Davis, 1989). Effort Expectancy (EE) is related to both the effort needed to use a technology product, how easy or difficult the technology is to use (Tak and Panwar, 2017). The original UTAUT model claims that the Effort Expectancy component is pertinent for behavioral intentions, but only for inexperienced users. The construct becomes less important as people master the technology and learn more about it (Venkatesh et al., 2003). For that reason, H2 is proposed as:

H2. Effort Expectancy (EE) has a positive impact on E-commerce intention to use.

2.1.3. Social Influence (SI)

Social Influence (SI) is defined as a level that demonstrates how someone views a significant individual and their belief that someone should utilize the technology product (Venkatesh et al., 2003). The idea of social influence and demonstrated how it affects how users' behavior is shaped (Huang and Kao, 2014). The adoption of a technology product is influenced by social concepts outside of one's control of thought (Rogers, 2010). Based on these findings, H2 is proposed as:

H3. Social Influence (SI) has a positive impact on E-commerce intention to use.

2.1.4. Facilitating Condition (FC)

A technology product's facilitating condition (FC) is defined as the extent to which one is confident that the infrastructure supports its utilization (Venkatesh et al., 2003). According to studies, using a PC, smartphone, credit card, or other device to execute a transaction or make a payment can increase one's usage intention (Taylor and Todd, 1995). Based on the previous studies, H4 is proposed as:

H4. Facilitating Condition (FC) has a positive impact on E-commerce intention to use.

 

 

2.1.5. Hedonic Motivation (HM)

Hedonic Motivation (HM) is the term used to describe the enjoyment or amusement that people derive from using a technological product (Venkatesh et al., 2012). According to Brown and Venkatesh (2005), HM is the key factor in deciding how a technological product is used and received. It has been noted that in the context of customers, intrinsic characteristics like fun and enjoyment have a big impact on the customer's attitude toward the new technology (Dabholkar and Bagozzi, 2002, Hwang and Kim, 2007). Thus, the following hypothesis is posited:

H5. Hedonic Motivation (HM) has a positive impact on E-commerce intention to use.

2.1.6. Trust (TR)

According to Gefen et al., 2003, trust refers to a person's willingness to rely on another person due to that person's conviction in that person's honesty, integrity, and competence. It has been recognized as a crucial element that influences a customer's propensity to accept internet banking (Flavi�n et al., 2006; Akhlaq and Ahmed, 2013). This implies that trust and the acceptance or adoption of using e-commerce are related. The higher level of trust in using or shopping in the e-commerce, the more consumer will shift from traditional market to e-commerce. Hence, H2 is proposed as:

H6. Hedonic Motivation (HM) has a positive impact on E-commerce intention to use.

2.1.7. Habit (HB)

Habit (HB) is defined as the extent to which users exhibit natural behavior as a result of prior learning, that recurring behavior that occasionally happens subconsciously and is influenced by experiences, knowledge, and abilities that are acquired over time (Limayem et al., 2007). According to Venkatesh et al. (2012), a habit is an action performed regularly by a person due to their expertise. Davis and Venkatesh (2004) identified habit as a different component that influences behavioral intention and technology use. There are empirical findings that demonstrate how different fundamental behaviors are influenced by habit when people utilize technology (Venkatesh et al., 2012). For that reason, H3 is proposed as:

H7. Habit (HB) has a positive impact on E-commerce intention to use.

2.2. Drivers to E-commmerce: Herd Behavior model towards E-commerce

Herd behavior, according to Hirshleifer and Teoh (2003), is characterized by persons who blindly follow the choices made by others. One of the factors that explained herd behavior is imitating others. Imitating others explained as the extent to which one follows previous adopters in selecting a particular form of technology.

Herd behavior differs significantly from previous theories of social influence. Herd behavior relies on monitoring other people's behavior and is unaffected by what other people think about the decision, like in the case of technology users (Sun, 2013). Whether these judgments are based on copying others, the goal of herd behavior is to support the choices that can produce the best outcomes for the user. Evidence from empirical studies shows the significance of herd behavior in consumer decision-making and technological uptake (Duan et al., 2009). According to research by Hong et al. (2017), herd behavior has a beneficial impact on how something is perceived to be used as well as a mediating role in how satisfied users are with their experiences.

The degree to which a person will follow other people's decisions while embracing technology is defined as imitating others (IO) (Venkatesh et al., 2012). Imitating past users reduces uncertainty and lowers the cost of knowledge acquisition. According to the herd behavior concept, it is rational to simply follow the herd in ambiguous situations rather than devoting one's own time and energy to weighing the options. This strategy is predicated on the idea that the present herd members have carefully considered their options and come to the conclusion that adopting the popular technology is the most sensible course of action. (Sun, 2013). Based on this discussion, H8 is developed:

H8. Imitating Others (IO) has a positive impact on E-commerce intention to use.

2.3. Barriers to E-commmerce adoption and promoting physical shop

Consumer spending through digital channels has seen a considerable change as a result of the development of smartphones, social media, and networking websites, as well as the growth of new sales and distribution methods. Customers now have more power thanks to a significant shift in retail innovation in terms of speed, customer service, reasonably priced goods and services, and on-time deliveries (Prachaseree et al., 2019). Compared to traditional retail, online shopping offers developing businesses more affordable, convenient, and accessible ways to find a variety of goods (Nguyen et al., 2019).

Therefore, e-commerce is said as the best solution in the pandemic era in order to evoke industry without making the outbreak spreading even wider. However, to be able to switch from traditional commerce to e-commerce. There will be some concerns. Thus, determining the obstacles that consumers consider when opting to use internet commerce to satisfy their demands is crucial. This study will adopt fear of technology, perceived risk, switching costs, and privacy concerns as the main barriers.

2.3.1. Privacy Risk (PV)

Accordingly, the definition of online privacy is the exchange of personal data about Internet users for specific advantages (Ashworth et al., 2006). The term "online privacy," on the other side, is frequently connected to information privacy and is therefore defined as Internet users' worries about their capacity to control the collection of their personal information as well as the usage of the information that will be used in the future and was generated based on their online activities (Castaneda et al., 2007).

The research by M. Alrawad et al. (2022) found that there are negative risks related to privacy that can affect customer intentions to use e-commerce due to concerns that service providers cannot protect their personal information or can even misuse their data and disseminate it on the internet. People's privacy concerns grow when the misuse of personal data (such credit card and bank account details) rises. It is lead into the evident that some have suggested that even though users may view the Internet as a marketing tool, security and privacy worries significantly influence whether people decide to make transactions online (Smith and Rupp, 2002). A person's propensity to be concerned about their online privacy will alter how they view a specific privacy issue given the rise in online identity theft and fraud (Cockcroft et al., 2005). Seeing this, H9 and H10 are formed:

H9. Privacy Risk (PV) has a negative impact on E-commerce intention to use.

H10. Privacy Risk (PV) has a positive impact on Physical shop intention.

2.3.2. Switch Cost (SC)

According to the literature of economy, switching costs are regarded as investments made by buyers and suppliers that are correlation-specific (Farrell and Shapiro, 1988). Weiss and Anderson (1992), Ping (1993), and others define switching costs as an overall cost or difficulty of switching in buyer-supplier relationships, an additional cost and effort when changing suppliers, and investments that restrict change (Nelson, 1996). Whereas, Burnham et al. (2003) explained that, switching costs are one-time expenses that clients incur when transferring their business to another provider. In addition to that, Jones et al. (2000) stated that, switching costs are the perceived financial and psychological expenses incurred when switching between options. Furthermore, Chen and Hitt (2002) describe switching costs as any perceived disutility that individual might experience as a result of moving service providers. Thus, H11 and H12 are formed:

H11. Switch Cost (SC) has a negative impact on E-commerce intention to use.

H12. Switch Cost (SC) has a positive impact on Physical shop intention.

2.3.3. Perceived Risk (PC)

The idea of risk has come to be recognized as one of the primary factors influencing customer choice. According to Bauer (1960), customer�s subjective perceptions of potential risks are what influence their behavior, not risks that exist. When two factors uncertainty and (bad) consequences are present, customers are starting to notice it (Cox and Rich, 1964). The perceived risks increase as uncertainty and bad outcomes increase (Hong and Cha, 2013). In addition, Ahmed et al. (2021) defines perceived risk as the degree of uncertainty brought on by threats to privacy and security as a result of prior encounters with unfavorable results following online buying.

Customers' perceptions of risk in e-commerce are noticeably higher than in traditional firms (Lee and Tan, 2003). They were not permitted to see, handle, or test the products or services they would buy. Customers may also experience transaction safety and privacy threats because of their worry that other careless parties might use their personal information when they make a purchase (Zhou et. al., 2008). Another customer concern is that they have trouble addressing complaints about faulty goods, service guarantees, and promotional frauds when they successfully make purchases on social media (Yu et. al., 2007; Zhang et. al., 2012). Hence, H13 and H14 are formed:

H13. Perceived Risk (PC) has a negative impact on E-commerce intention to use.

H14. Perceived Risk (PC) has a positive impact on Physical shop intention.

2.3.4. Technology Fear (TF)

Both technology and customer perceptions about it are constantly evolving (Schumpeter, 2012). The constant introduction of new technical items, marked by their excessive complexity and quick development, poses a threat to fill many consumers with anxiety, uncertainty, and doubt (Yadav et al., 2006). The avoidance of technology is known as "technophobia," and it results from worry and fear. E-commerce use may be lessened as a result of a fear of the negative effects of utilizing it, which might prevent its acceptance and increase feelings of insecurity and intimidation. In addition, research has shown that fear is a significant impediment to using.

In Hsiao's (2001) study, on a particular e-marketplace experience, it is suggested that four factors such as lack of knowledge with B2B e-commerce, risk aversion, lack of confidence in trade partners, and inconsistent cultural practices are considerably more significant adoption barriers than technology fear. At first look, the idea of technological dread and the idea of ignorance are identical. Thus, H15 and H16 are formed:

H15. Technology Fear (TF) has a negative impact on E-commerce intention to use.

H16. Technology Fear (TF) has a positive impact on Physical shop intention.

Barriers to using e-commerce can be different in each region. Users in Spain prefer to continue buying at physical stores instead of switching to e-commerce due to barriers to switching costs and perceived risk, while users in Portugal consider barriers to privacy cost and technology fear (E. Higueras-Castillo et al., 2023). In this study, researchers want to examine what barriers are considered by users in Indonesia in using e-commerce.

 

RESEARCH METHOD

In this research, it consists of 501 sample individuals who have purchased fashion items online. The sampling technique used in this research is Probability Sampling, which is a technique that provides equal opportunities for the research population to make it as sample (Stock et al, 2019). Out of 501 respondents which acts as research population, 487 respondents were selected as research samples.

The research was conducted through a questionnaire with 51 measurement items to measure a total of fourteen variables consisting of twelve independent variables and two dependent variables. This research uses Quantitative Research Method which practice assessable data and statistics figures within analysis using SPSS Statistic using the primary data.

The data is coded with values according to the respondent�s answers on inquiry form rated from scale 1 to 5. Descriptive statistics are used to analyze the demographic characteristics of the respondents. Moreover, the data collected has went under validity and reliability test before hypothesis being tested using regression analysis. Validity testing was carried out using Exploratory Factor Analysis (EFA), and continued with Cronbach's Alpha to test reliability. Hypothesis testing to test the effect of the independent variables on the dependent variable was carried out using multiple regression analysis.

Research Model is defined as a visual portrayal or research abstraction which contains variables used within research and their relations with other variables (Salminen et al, 2020). The Model (firgure 1) shown in this research signifies the means of "The Intention of Consumer Behavior to use Online Shopping in Buying Fashion post "PPKM" Policy in Indonesia". These following image presents the research model:

The variables in this study are divided into drivers and barriers. Drivers consist of UTAUT 2 and herd behavior. In the drivers� section, there are 8 variables including performance expectancy, effort expectancy, social influence, habit, facilitation conditions, hedonic motivation, trust, and imitating others. Meanwhile, the barriers section is divided into 4 sections, namely privacy, switching costs, perceived risks, and technological fear. All independent variables were analyzed for their influence on E-Commerce Intention, whereas barriers were analyzed for their influence on Physical Shop Intention variable.

 

 

 

Figure 1. Research Model

Source: Researcher, 2023

 

RESULTS AND DICCUSSION

4.1. Sociodemographic data

Demographic data shows the characteristics of the respondents in this research based on gender and age. In total there are 487 respondents. The results showed that the sample consisted of 35.3% male, namely 172 respondents and 64.7% female or 315 respondents.

Furthermore, data based on age showed that respondents were dominated by participants aged 20-35 years as much as 62.4% with a total of 304 respondents. Followed by participants ranged from 36-50 old, with total 150 respondents (30.8%). There were also 3.7% participants under 20 years old and 3.1% over 50 years old with 18 and 15 respondents respectively.

 

Table 1. Demographic Characteristics of the Respondents

 

4.1 Validity Test

The validity test in this study was carried out with data every variable so that one could see the validity of the instrument for each variable item. Thus, invalidities can be

�detected in a short time and items can be immediately replaced or eliminated. The Kaiser-Meyer-Olkin (KMO) used in this study to measure the sampling adequacy. The criterion value is above 0.5 to be considered as good (Hair et al., 2019; Damasio, 2012). Following are the results of the validity test using the KMO and Bartlett's Test conducted by the researcher:

The test results (Table 2) show that the KMO values for all variables are considered valid because they have KMO values greater than 0.5, significance values less than 0.05 and eigenvalues greater than 1. KMO values for all variables in this study range from 0.622 � 0.931. While Bartlett's significance value ranges from 0.000 to all items. Furthermore, the eigenvalues range from 1,908 � 5,552. Thus, it can be concluded that the items in the questionnaire used in this study are valid and legitimate to be used in measuring the intention of consumer behavior and the factors that influence it.

 

4.2 Reliability Test

In this research, the reliability test was carried out using Cronbach�s Alpha and testing per research variable. There are a total of 14 reliability tests in describing whether the items contained in the research instrument in the form of a questionnaire are reliable.

The results in Table 2 showed that the Cronbach�s Alpha values for all research variables ranged from 0.712 � 0.949. In the PC, SC, IO and PS variables, the Cronbach Alpha value is included in the reliable category. Whereas for other variables, namely PE, EE, SI, FC, HM, HB, TR, TF, PV, and EI, the Cronbach Alpha value is in the very reliable category. Thus, it can be concluded that all items in the questionnaire are reliable and the instrument is considered capable of measuring consistently.

 

Table 2. Measurement of Scale and Exploratory Factor Analysis

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4.3 Regression Analysis

Regression analysis was carried out in measuring the effect of the independent variables in this study, namely as many as 12 variables on 2 dependent variables, namely e- commerce intention to use and physical shop intention. The test was carried out by means of regression analysis with the following results:

 

 

 

 

 

Table 3.� Multiple Regression Coefficient and Hypothesis Testing H1 and H2

 

The result shows a significance value of 0.000 so that it can be interpreted that the effect of the independent variable on the dependent variable is significant. The test results show that the variables Performance Expectancy, Habit, Imitating Others have a positive effect on Ecommerce Intention Variables with respective values of 3.456, 8.381, and 2.241 and the Technology Fear variable has a negative effect on Ecommerce Intention with a t value of - 3.499.

Meanwhile, for the PS variable, the test shows significant <0.05, which is equal to 0.000, thus the effect of the independent variables is significant. From the results of the Regression Analysis, the Privacy Risk Variable has a positive effect on the Physical Shop Intention Variable with a t value of 2.005, while the Hedonic motivation variable has a negative effect on Physical Shop Intention with a t value = 8.381.

 

Table 4. Summary of drivers (UTAUT 2 and Herd Behavior) and barriers for Indonesian shoppers

 

E-commerce Intention

Physical Shop Intention

Factors that influence Indonesian shoppers

Positive effect:

Performance expectancy, habits, imitating others

 

Negative effect: technology fear

Positive effect:

Privacy Risk

 

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CONCLUSION

The data shows that people are increasingly avoiding physical shop purchases and shifting to digital Shop (Hammouri, 2021). In this research, we are trying to look the potential market of online and physical shop after �PPKM� Policy in Indonesia.

Performance Expectancy, Habit, Imitating Others have a positive effect on Ecommerce Variables. This is because consumers' shopping habits during a pandemic can significantly influence their desire to shop in the future. Furthermore, the expectation of the product to be purchased can determine the consumer's desire to buy. Apart from that, psychologically seeing things that other people have can influence someone to buy the same thing because of a high sense of interest. Technology Fear variable has a negative effect on Ecommerce Intention. This is because the fear of technology being unable to significantly make someone afraid of making transactions online. On the other hand, low prices and demand for products overcame customers' fear of buying goods online. Privacy Risk Variable has a positive effect on the Physical Shop Intention Variable. because for certain items, customers sometimes cover it up and tend not to want someone to know that they are buying it so they choose to shop physically. Online shopping has a high privacy risk due to purchase history. Hedonic motivation variable has a negative effect on Physical Shop Intention, because basically a physical store does not always have values to carry out hedonic actions. On the contrary, sometimes shopping online actually encourages customers to spend more so that they are more hedonistic.

This study aims to bring theoretical contribution by analyzing the drivers and barriers factors that influence customers� intention to use e-commerce or preferences to buy in physical stores. The study reveals that the variables influencing the formation of segments when predicting� behaviours by adding herd behaviours factors to define customer behaviour towards e-commerce vs physical shop.

This studycan be used as a recommendation for business owners to have more understanding on Indonesian market towards online shopping after Post-Covid policy. The research shows that technology fear needs to be considered as an important factor when developing e-commerce platforms, and factors such as performance expectancy, habits, and people behaviors on imitating others are having positive impact to e-commerce performance. On the other hand, privacy risk factor can be seen as an opportunity for business owners to keep the offline stores.

This research is limited in the time of research for a fairly-wide number of variables. In future research, the researcher provides suggestions for further researchers to use a different research period, or to conduct a longitudinal study to analyze more deeply the influence of the variables in this study. In addition, it is possible to add other variables such as promotions and discounts to analyze the influence of the variables in online purchase intention. Further researchers can consider to use different data analysis methods to observe the effect of independent variables on e-commerce intention and physical shop intention.

 

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Copyright holders:

Amelya Pane-Citra, Suwanto Ade, Eliahannanti Firdha, Harianto V. Monica (2023)

 

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Journal of Syntax Admiration

 

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