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The Effect of Perceived Benefits and Trust on Gen Z’s Online Shopping Behavior

Nguyen Thị Loan

Faculty of Economics and Business Administration, Hong Duc University, Thanh Hoa City, Vietnam

Abstract: Generation Z (Gen Z) is becoming the global economy's future-oriented labor and consumer force with innovative, different characteristics, and often creating new trends. In Vietnam, Gen Z accounts for nearly 21% of the population, so it is gradually becoming a potential market segment and changing consumer trends. This study was conducted to assess the impact of the perceived benefits on the online shopping behavior of generation Z to recommend attracting more Gen Z buyers. The quantitative method was applied, and the prototype was conducted on a sample scale of 374 Gen Z organisms in Thanh Hoa province, Vietnam. Data was collected through the Google form and analyzed by Smart PLS-SEM software. Research results indicate that the perceived benefits impact the trust in shopping and thereby strongly impact the online buying behavior of Gen Z.

Keywords: Gen Z, shopping behavior, online, perceived benefits

Received June 02, 2022; Revised July 07, 2022; Accepted July 15, 2022

Citation: Nguyen Thị Loan, The Effect of Perceived Benefits and Trust on Gen Z’s Online Shopping Behavior. Journal of Behavioural Economics Finance Entrepreneurship Accounting and Transport, 10 (1). 10-16. January 2022. doi:10.12691/jbe-10-1-2

1. Introduction

Along with the development of technology and the internet, online shopping has become a virtual shopping channel alongside traditional shopping methods. Data in E-commerce White Paper in 2021 shows that the percentage of internet users participating in online shopping has increased from 77% in 2019 to 88% in 2020, reaching the size of the retail e-commerce market with a value of $11.8 billion and expected to increase by 20% in 2021 [1]. Gen Z is the generation of people born in the digital age, exposed to technological devices such as personal computers, tablets, and mobile phones; they grew up with the growth of social networks and the digital and electronic world. They profoundly influence the trends and user behavior at present and in the future, so retailers and researchers are trying to understand and analyze the needs and shopping behavior of Gen Z to get access and conquer this group of potential customers in online shopping.

Research on online shopping behavior in general and online shopping of Gen Z is an exciting subject for international researchers very early, with various approaches and criteria to be evaluated [2]. Accordingly to Li & Zhang [3], the factors affecting online shopping behavior include external environment, demographics, and personal characteristics. Rajagopal [2] said that personal characteristics (gender, age) and facilities conditions (internet and diversity of goods) are two main groups of factors affecting the intentions and decisions of Gen Z's online shopping. Chandra & Sinha (2013), based on the TBP model, believe that demographics, desires, online purchasing attitude, subjective standards, and behavioral awareness are the factors affecting online shopping. From the same point of view, Trung & Hà [4] confirms that demographics, desires, online shopping attitude, subjective standards, and behavioral awareness are the factors affecting online shopping. Hạnh et al. [5] think that relevance, diversity, sense of time, convenience, promotion, and easy comparison positively impact young people's online shopping. Jaiswal & Singh [6] determine that diversity, low price, trust, promotion, time, comparison, attitude, convenience, ease of use, and customer service are important factors affecting the online shopping behavior of young people.

In other studies by Châu & Đào [7], it was pointed out that financial and product risk factors, variety of goods selection, trust, the responsiveness of the website, time risk, comfort, convenience, and price influence the decision to continue shopping online of consumers. Hương et al. [8] point out that the factor that most positively impacts attitudes is the perception of customers about services and infrastructure, while the perception of financial risks strongly impacts in a negative direction on the attitude of buyers. According to Thắng & Độ [9], the attitude and perception of controlling consumer behavior positively influence the intention to buy online. Tráng & Tiến [10] and Hạnh et al. [5] analyze the influence of the website's status, subjective standards, behavior control, emotional risks, beliefs, quality of the website, and prices on the online shopping behavior of young people. Hương et al. [8] said that the online shopping behavior of urban and young people also has an intersection in the results. More prominent are convenience, variety of goods, shopping belief, product price, shopping risk, and subjective standards that have the most decisive impact on online shopping behavior. Thành & Ơn [11] said that generation Z is very sensitive to technology, so reading and referring to comments is also an important factor affecting their buying behavior. The literature review concludes that there are three groups of factors that affect the online shopping behavior of Gen Z, including (1) perceived benefit factors such as convenience, diversification of goods, and responsiveness of shopping platforms; (2) perceived risk factors such as financial risk, purchase risk, and payment risk; (3) personal behavior perception such as feeling trust and trust in online shopping. In this study, the author applies the TBP behavior planning theory, which focuses on analyzing the influence of perceived benefit, and trust in the online shopping behavior of Generation Z.

2. Literature Review and Hypothesis Development

2.1. Convenience

The sense of convenience is understood that when Gen Z wants to shop for goods, there is no need to move or spend time searching and selecting goods [12]. Instead, they sit anywhere with internet waves in a few simple moves to enjoy shopping. In addition, Gen Z also feels the convenience of payment, such as transfers, electronic wallets, and reward points, which creates attraction in the consumer experience of Gen Z. The research results of Vijay & Balaji (2009), Jadhav & Khanna, (2016) [13], Châu & Đào (2014) [7], Long (2018) [14], and Rishi (2020) [15] all confirm that convenience perception strongly influences trust in shopping, thereby affecting the online shopping behavior of the young generation.

Hypothesis H1: Convenience has a reversible impact on the online shopping trust of Gen Z.

2.2. The Diversity of Goods

Diversified goods mean that most goods can be purchased online as in traditional shopping. Even more, they can accessible access diversified goods, not limited by geographical scope. The variety of goods is also understood as the number and types of products accessible in online shopping [16].

It is clear that access criterion, the online market will be more attractive than the traditional market, especially in the current period when disease outbreaks, travel restrictions, and travel costs between areas become increasingly expensive due to the rising price of petroleum and the shrinking shopping time, the access to goods on the online channel will be much more diversified and convenient [17]. Therefore, Gen Z will feel a greater variety of goods in the online shopping process than in other traditional shopping methods, which promotes the trust and future shopping behavior of Gen Z [7,13,18].

Hypothesis H2: The diversity of goods positively impacts the online shopping trust of Gen Z.

2.3. Website Responsiveness (OF)

Website responsiveness is the ability to provide services to many buyers simultaneously. Paiva et al. [19] show that website responsiveness is the speed of order processing, ease of purchase, shopping, comparing, and selecting goods online [7]. Rishi [15] evaluated from the perspective of accessibility and convenience of the purchase website, especially the suggestion of similar items and optimization experience to Gen Z. Website responsiveness strongly influences Gen Z's decision to visit and buy. If the website responds well, giving customers a good shopping experience and customer care will be prioritized by generation Z [5,13,20].

Hypothesis H3: The website responsiveness positively impacts the online shopping trust of Gen Z.

2.4. Online Shopping Trust

Online shopping trust means customers' peace of mind and trust when shopping online. They feel this is an address that can be trusted, not afraid of being cheated or selling goods wrongly with advertising. According to research by Deloite (2021), Vietnamese consumers in general and generation Z, in particular, are increasingly believing in this modern form of shopping; the more they believe, the higher increases the frequency of online shopping choices of generation Z Jadhav & Khanna [13]. In addition, the service provider is also working to provide customers with the most convenient services and policies to support the sale and return of goods for customers to trust more and be more satisfied. The programs and promotions are also more actually, so customers do not have hallucinations when buying online. Gen Z's online shopping trust is tested by considering the positive feedback of people who have bought before or the appearance of relatives in the customer list of sellers [7], [13]. When online shopping positively impacts consumers, they will have online trust in shopping, merchants, and online payment methods [7]. Then they will continue to buy online and introduce friends and relatives to use this purchase. In addition, when the role of online shopping channels is recognized and developed, it will attract the attention of Consumer Rights Protection Agencies and communities, thereby creating a trust for the young generation when shopping.

Hypothesis H4: Online shopping trust positively impacts the online shopping behavior of Gen Z.

2.5. Online Shopping Behavior

Gen Z's online shopping behavior is understood as all behavioral manifestations from forming the intention to buy, choosing to buy, and purchasing decision-making. Shopping behavior is choosing to analyze the item, conditions, and methods of purchase that will gradually form trust and purchase decisions. Each customer group will have different buying behavior, for generation Z is very tech-savvy and will seek to survey the sales activity of suppliers through reading customer comments, visiting the company's website before buying, or seeking expert advice online before buying a valuable product. In addition, the use of reference groups is also applied by Gen Z, such as consulting family and friends before buying [7,13].

3. Research Model

In this study, the author examines the above assumptions by using the theory of planned behavior as the theoretical basis and testing part of the theoretical framework in the Thanh Hoa market. Based on the previously studied models and results and the specific properties of Gen Z, the researcher proposes a research model as follows.

Figure 1. Theoretical Framework

4. Methodology

4.1. Measures

Based on inheriting the research scale of Châu & Đào (2014), Hương et al. (2016); Thắng and Độ (2016), Long (2018), and Jaiswal & Singh (2020), combined with the analysis result of Gen Z's characterization, the author develops the list of scales and doing an in-depth interview with experts. The scales used in the study are as follows:

4.2. Data and Sample

To ensure reliability and convenience, the authors conducted non-random sampling conveniently. However, for the survey data to be highly representative, the authors carefully selected the survey subjects before sending the survey. Specifically, the sample was selected at universities, high schools, and lower secondary schools in Thanh Hoa province; the survey was sent through acquaintances and friends and published on Facebook by members of the research team and teachers. https://forms.gle/TCfWSj8VXRfZJ8ax5 (link to online survey table) from 16/2/2022 to 27/3/3022. In addition, the authors sent the survey to students in the university and sent the survey to those who left school and went to work to make the survey results objective and uniform.

The research sample is selected scientifically, ensuring the representativeness and diversity of industries and types of business. The optimal sample size depends on the reliability expectations, data analysis method, estimation method used in the study and parameters to be estimated. The optimal sample size depends on the reliability expectations, data analysis method, estimation method used in the study and parameters to be estimated. This study uses PLS-SEM software, so it should have at least 05 -10 observations for one variable [21]. With a total observation variable of 25, the research group decided to select the survey sample with a minimum sample size equal to 15 times the observation variable = 375. This means the study will end when 375 or more votes are collected to ensure reliability and representativeness. By the end of 3/27/2022, the research team received 385 responses and closed the survey process at 17: 00 on the same day. After filtering the remaining non-conforming votes, 374 votes were eligible for analysis.

5. Research Results and Discussion

5.1. Statistics for Data Collection

The survey results show that the percentage of women who shop online is more than men, aged 18-24 account for the highest 58%, mainly high school students and students aged 16-18 and secondary and high school students. This makes perfect sense because students are very active and like to explore new things early on, so they actively go to find jobs and work more subsidized by their families, and spend quite a lot. The proportion of people with income accounts for 80% of which income over 2 million accounts for 50%. This is consistent between the surveyor, the interviewer, and the demographic reality of Gen Z.

Table 1. Respondents' Characteristics

5.2. Testing of Measurement Models

In order to evaluate the appropriateness of the scales, the author group the value of the factor, the information is consistent with the scale; the value are important indicators to check the reliability. The data analysis showed that all scales ensured reliability and correlation value indicators. The loading factor is > 0.7 (except for HVMH1, HVMH4, and STT3 lines have loading factors of 6.42, 6.87, and 6.77); however, according to Hair et al., the Outer Loading factor is>0.6 in some cases without poly-linearity is acceptable and there is no significant difference. Composite Reliability values from [0.851-0.926] and Cronbach's alpha number [0.78-0.900] satisfy the condition that > 0.7 for all elements in the model achieves consistency at [21]. The AVE value of each structure model is a range of [0.535- 0.751] all >0.5 to conclude that all scales reach the maximum value [22].

Table 2. The results of the evaluation and measurement of the scale

To evaluate the values of the structures in the model, the authors used the HTMT index. Table 2 shows that the factors have an HTMT value of <0.9 which ensures a good value between the relevant structures in the study model [21].

Table 3. Hypothesis test

Therefore, the test results show that the scales at the top of the new study model ensure this information, the maximum value and the minimum value.

5.3. Test of Structure Model

Hair et al. (2016) proposed the method of sample magnification (bootstrapping N = 5000) in the PLS-SEM analysis technique to find the standard error of the parameter under observation so that the value of the hypothesis can be concluded. In this study, at a sample size of 374, the test result used a 5% significant level for evaluation. The result is as follows:

Figure 2. PLS-SEM Analysis Result

5.4. Discussion

The analysis results show the three groups of factors: Convenience, diversity in goods selection, and responsiveness of the website all have a substantial impact on the online shopping belief of generation Z in Thanh Hoa province with impact weights of 0.329; 0.377, and 0.219 respectively, there the diversification of goods has the most impact. Gen Z is very interested in the convenience and variety of goods while online buying is not limited by geographical scope; Gen Z can buy from sellers in other provinces, cities, and abroad. Therefore, entrepreneurs need to be aware that to have a suitable business strategy, e-commerce brokers also need to provide a variety of partners and sellers to increase customer choice. The research results are similar to those of Vijay & Balaji (2009), Jadhav & Khanna (2016), Marza et al. (2019), Rishi (2020), and Cai et al. (2022).

They are considering the relationship between trust and online shopping behavior; the higher the trust and the greater the decision to buy online. In other words, trust in shopping has a reversible relationship with shopping behavior with an impact factor of 0.81. The results are similar to previous studies by Jadhav & Khanna [13]; Châu & Đào [7].

6. Conclusion

In online shopping, Gen Z has unique characteristics and thinks about shopping, unlike ordinary consumers.

Thus, businesses may need to focus on their characteristics to offer appropriate solutions and policies to attract and stimulate the shopping of generation Z. The research result shows that all three factors have been identified, including convenience, diversity of goods, and responsiveness of the website have a direct impact on the trust and online shopping behavior of generation Z. Based on the analysis results, the authors propose some recommendations to improve business efficiency for enterprises. Firstly, expand and further developments in the online shopping market. Secondly, create optimal conditions, encouraging and helping enterprises of the province to carry out sales activities effectively. Thirdly, creating an enabling environment for e-commerce and online trading in the province expanded and developed through pilot projects. Fourth, establish a communication network infrastructure for accessible and low-cost consumers based on open standards to ensure continuity and interoperability. Last but not least, introducing policies encourages enterprises in online trading activities to have the best conditions in the current conditions.

Source of Funding

This research did not receive any grant from public, commercial, or not-for-profit funding agencies.

Competing Interests Statement

The authors declare no competing financial, professional, or personal interests.

Consent for Publication

The authors declare that they consented to the publication of this research work.

References

[1]   | IDEA, “E-commerce White Paper,” 2021. [Online]. Available: www.idea.gov.vn.
[2]   | Rajagopal, “Impact of radio advertisements on buying behavior of urban commuters,” Int. J. Retail Distrib. Manag., vol. 39, no. 7, pp. 480-503, 2011.
[3]   | N. Li and P. Zhang, “Consumer online shopping attitudes and behavior: An assessment of research,” Eighth Am. Conf. Inf. Syst., no. October 2002, pp. 508-517, 2002.
[4]   | Phạm Quốc Trung and Nguyễn Ngọc Hải Hà, “Yếu tố tác động lên sự thôi thúc mua hàng ngẫu hứng trực tuyến của người tiêu tại TP.HCM,” Tạp chí khoa học Đại học Mở Thành phố Hồ Chí Minh, vol. 12, no. 3, 2017.
[5]   | Vũ Thị Hạnh, N. N. Anh, V. H. Phương, and N. H. T. My, “Các Yếu Tố Ảnh Hưởng Đến Hành Vi Mua Sắm Trực Tuyến Của Sinh Viên Trên Địa Bàn Thành Phố Hà Nội Trong Bối Cảnh Covid-19,” Tạp chí Quản lý và Kinh tế quốc tế, vol. 141, 2022, [Online]. Available: http://tapchi.ftu.edu.vn.
[6]   | S. Jaiswal and A. Singh, “Influence of the Determinants of Online Customer Experience on Online Customer Satisfaction,” Paradigm, vol. 24, no. 1, pp. 41-55, 2020.
[7]   | Nguyễn Thị Bảo Châu and Lê Nguyễn Xuân Đào, “Phân tích các nhân tố ảnh hưởng đến hành vi mua sắm trực tuyến của người tiêu dùng Thành phố Cần Thơ.,” Tạp chí Khoa học Trường Đại học Cần Thơ., vol. 2, no. 4, 2014.
[8]   | N. H. D. Hương, N. T. B. Minh, and T. Nguyễn Ngọc Bích, “Nghiên cứu các yếu tố ảnh hưởng đến thái độ của người mua trong thị trường thương mại điện tử,” Tạp Chí Phát Triển Khoa học và côngnghệ, vol. 19, no. Q4-2016, pp. 68-80, 2016.
[9]   | Hà Ngọc Thắng and Nguyễn Thành Độ, “Các yếu tố ảnh hưởng đến ý định mua sắm trực tuyến của người tiêu dùng Việt Nam: Nghiên cứu mở rộng thuyết hành vi có hoạch định Hà,” Tạp chí Khoa học ĐHQGHN Kinh tế và Kinh doanh, vol. 32, no. 4, pp. 21-28, 2016.
[10]   | Bùi Thanh Tráng and Hồ Xuân Tiến, “Thương mại trực tuyến và hành vi mua sắm của người tiêu dùng,” Tạp chí Công thương, vol. 5, no. Online, 2020.
[11]   | T. Văn Thành and Đ. Xuân Ơn, “Các nhân tố ảnh hưởng đến ý định mua sắm trực tuyến của người tiêu dùng Thế hệ Z tại Việt Nam,” Tạp chí Khoa học Đào tạo Ngân hàng, pp. 27-35, 2021.
[12]   | Q. T. Pham, X. P. Tran, S. Misra, R. Maskeliunas, and R. Damaševičius, “Relationship between convenience, perceived value, and repurchase intention in online shopping in Vietnam,” Sustain., vol. 10, no. 1, 2018.
[13]   | V. Jadhav and M. Khanna, “Factors influencing online buying behavior of college students: A qualitative analysis,” Qual. Rep., vol. 21, no. 1, pp. 1-15, 2016.
[14]   | Đào Mạnh Long, “Các nhân tố tác động đến hành vi mua sắm quần áo trực tuyến của khách hàng tại khu vực TP.HCM. Thành phố Hồ Chí Minh:,” Tạp chí khoa học Trường Đại học Kinh tế TP.HCM, vol. 3, no. 5, 2018.
[15]   | B. J. Rishi, “An Empirical Study of Online Shopping Behaviour - A Factor Analysis Approach,” J. Mark. Commun., vol. 3, no. 3, pp. 40-49, 2020.
[16]   | S. Marza, I. Idris, and A. Abror, “The Influence of Convenience, Enjoyment, Perceived Risk, And Trust On The Attitude Toward Online Shopping,” vol. 64, no. 2001, pp. 589-598, 2019.
[17]   | V. Kumar and U. Dange, “A Study of Factors Affecting Online Buying Behavior: A Conceptual Model,” SSRN Electron. J., no. January 2012, 2013.
[18]   | C. H. Park and Y. G. Kim, “Identifying key factors affecting consumer purchase behavior in an online shopping context,” Int. J. Retail Distrib. Manag., vol. 31, no. 1, pp. 16-29, 2003.
[19]   | L. E. B. Paiva, E. S. Sousa, T. C. B. Lima, and D. Da Silva, “Planned behavior and religious beliefs as antecedents to entrepreneurial intention: A study with university students,” Rev. Adm. Mackenzie, vol. 21, no. 2, 2020.
[20]   | R. Cai, X. Y. Leung, and C. G. Q. Chi, “Ghost kitchens on the rise: Effects of knowledge and perceived benefit-risk on customers’ behavioral intentions,” Int. J. Hosp. Manag., vol. 101, no. November 2021, p. 103110, 2022.
[21]   | R. E. Joseph F. Hair, J., Black, W. C., Babin, B. J., & Anderson, Multivariate Data Analysis (8th ed.). United Kingdom: Cengage Learning EMEA, 2019.
[22]   | J. F. Hair, C. M. Ringle, and M. Sarstedt, “Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance,” Long Range Plann., vol. 46, no. 1-2, pp. 1-12, 2013.