Generation Z: Factors Influencing Online Purchase Behaviour in Kuala Lumpur and Selangor

Generation Z: Factors Influencing Online Purchase Behaviour in Kuala Lumpur and Selangor

Lee Yien Xing¹, Khaw Rou Ai², Uma Thevi Munikrishnan³, Nurfaradilla Haron⁴, Aeshah Mohd Ali^5*, Nor Aziyatul Izni⁶, Nur Ilyana Ismarau Tajuddin⁷

^1,2,3Department of Management Studies, Faculty of Business and Management, UCSI University, Malaysia

⁴Department of Economics, Islamic Finance & Muamalat, Academy of Contemporary Islamic Studies (ACIS), University Technology MARA (UiTM), Shah Alam, Selangor, Malaysia.

⁵Arshad Ayub Graduate Business School, University Technology MARA (UiTM), Shah Alam, Malaysia

⁶Centre of Foundation Studies, University Technology MARA, Cawangan Selangor, Kampus Dengkil, Dengkil, Selangor, Malaysia

⁷Pusat Tamhidi, University Sains Islam Malaysia, Nilai, Negeri Sembilan, Malaysia

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.90300134

Received: 26 February 2025; Accepted: 05 March 2025; Published: 04 April 2025

ABSTRACT

The “Shared Prosperity Vision 2030” and the Malaysia Digital Economy Plan predicted 500,000 jobs will be created and 22.6% of Malaysia’s GDP will come from the digital sector. Therefore, the aim of this research is to assess the factors (price, e-commerce purchase experience, security, perceived risk, and perceived benefit) influencing online purchase behaviour among Generation Z in Kuala Lumpur and Selangor. The Theory Planned Behaviour (TPB) was used to analyse the human behavioural decisions. The theory asserted the three main elements (attitude, subjective norms, and perceived behaviour control) will affect certain behavioural intentions of individuals. Four hundred (400) questionnaires were distributed on a recommended sample for Generation Z at selected Private HEIs in Kuala Lumpur and Selangor. The set of data collected was examined using IBM SPPS (version 23) for data analysis. The findings of this research resulted in a significant influence of price and perceived benefit towards online purchase behaviour among generation Z. Overall, price was the most significant factor (β = .230; p = .000 < .05), followed by perceived benefit (β = .218; p = .000 < .05). Price ranked first because when making a comparison of price, results often show that for Generation Z to purchase these products online would be cheaper. Meanwhile, for perceived benefit, online shopping is preferred by Generation Z since it is more convenient than offline shopping.

Keywords: Online Purchase Behaviour, Perceived Benefit, Price, Perceived Risk, Security.

INTRODUCTION

Electronic commerce (e-commerce) has set up a platform for people to trade online, where people can buy many desired goods such as food, clothes, sundry items, electrical apparatus, and medical-related products from the merchant. In the past, only large corporate organizations were interested in operating with technological advancements to improve marketing effectiveness, but today, many small-scale retailers have started to sell their goods and services online. Currently, there are already many famous online shopping platforms being set up locally and globally and it has created new opportunities for retailers and shaped a new pattern of retail (Boey, 2020). According to Pena-Garcia, Gil-Saura, Rodriguez- Orejuela, and Siqueira-Junior (2020), online purchase behaviour can be translated as a consumer who has a purchase intention.

Problem Of Study

Based on Digital News Asia, under the Malaysia Digital Economy Blueprint in “Shared Prosperity Vision 2030”, it is estimated that Malaysia’s digital economy will contribute 22.6% of its GDP and generate 500,000 employments. YAB Tan Sri Dato’ Haji Muhyiddin, a former prime minister stated, digital economy is expected to attract RM70 billion new digital sector investments within and outside the country (Edge Market,2021). According to this effort, the government pushes 875,000 micro, small, and medium-sized businesses to embrace e-commerce.

The rapid growth in digital economy, however, has led to the growing number of deceitful merchants manipulating the product’s price in various ways. According to Lydia (2021) these merchants create fake scenarios on the short supply of goods so that they can increase the price openly. According to the Domestic Trade and Consumer Affairs Minister, Datuk Seri Alexander Nanta Linggi, due to the continued growth of internet shops, unethical and illegal marketing strategies are becoming more prevalent, example during special promotion events, such as the “Black Friday sale” or the 11.11 sale (Pui Yin, 2021). Bavani (2020) in The Star July 20 highlighted a total of 2,287 complaints were received, a significant increase from the 5,415 instances reported for the entire year in 2019. According to Shamsul (2020), head of the Ministry’s Investigation, Exhibition and Operations Department in Consumer Kini’s report, most scams occurred in major cities such as Kuala Lumpur, Shah Alam, Johor Bahru and George Town with whopping 6,187 complaints involving millions of ringgits. Nevertheless, it is essential to read product reviews before purchasing a product, to ensure its legitimacy. However, merchants sometimes set up fake customer reviews on their sites to attract more people to place orders. In recent reports by CNBC, Amazon has taken initiative to identify fake reviews using their powerful learning machine. Up till now, they have removed 20 thousand fake reviews out of 10 million reviews submitted (Katie, 2020).

Due to the virtual nature of online transactions, it is subjected to certain perceived risk i.e., non-delivery risk. Online buyers are frequently worried about not receiving the product after purchasing it due to loss, damage, or scams, even if this is an exceptional circumstance (Tham, Dastane, Johari, & Ismail, 2019). For instance, some workers at a Malaysian logistic (J&T) express have exhibited lax attitudes such as carelessly throwing parcels, reflecting the risks faced by online consumers (Kaur, 2021). Meanwhile, Datuk Rosol (2021) asserted that most of the complaints received by the Ministry of Domestic Trade and Consumer Affairs are related to the poor condition of the goods purchased and lacks similarity from what was advertised.

Currently, online shopping offers many benefits to people i.e., convenience in terms of timesaving, ease of checking price comparisons, broader selections of products, ease of giving product evaluations, and no pressure on shopping, wherein people can buy almost anything without leaving their homes. However, alongside these benefits, there are also concerns such as misconduct and poor ethics by deceitful traders. Amirah (2021) indicated the figures from Malaysian Communications and Multimedia Commission (MCMC) has shown a drastic increase in online crime. For instance, traders deceive consumers by using promotional advertisements. The displayed product prices change after the promotional hours begin, causing consumers to complain as the product is displayed again at its original price, confirming false advertisement. An article by Pillay (2017) in News Straits Times June 4, highlighted there are scammers who set up fake websites to offer goods and services and disappear after payment is made. The supposed “benefit” and convenience of online purchasing turns out to be a “deficit” and inconvenience to the consumer.

Prior studies by Neger and Uddin, (2020); Ahmad, Azizan, Shuib, Md Nor, and Mat Noor, (2022.) have recommended to comprehensively explore the context of online purchase behaviour. It is plausible that there are other potential aspects that influence online purchase behaviour among Generation Z, such as price and security (Neger, & Uddin, 2020); and perceived risk (Ahmad et al, 2022.). E-commerce consumers survey report by MCMC recorded 3.9 percent of online shoppers are dissatisfied with their online purchasing experiences (Malaysian Communications and Multimedia Commission, 2018). Yew and Kamarulzaman, (2020) recommended exploring online perceived benefits in other states like Pulau Pinang, Johor Bharu and Selangor that has high Internet penetration. Furthermore, Isa, Annuar, Gisip, and Lajuni, (2020) recommended that future research should explore private universities context compared with public universities. In fact, some authors have stated to expand the study on purchase intention for other age groups such as Gen Z (JianAi, Sze, Fern, & Yu Wan, 2022). Various theorues have been employed by past researchers, Ching et al. (2021) focused on Technology Acceptance Model (TAM); Ambad et al. (2022) applied for Social Identity Theory (SIT) and Perceived Risk and Benefits Model (PRBM); and Isa et. al, (2020) focused on Online Shopping Acceptance Model (OSAM). Ching, Hasan and Hasan (2021); Ambad, Haron and Ishar (2022) suggested to explore other theories such as the theory of planned behaviour.

In line with the My Digital objective towards economic recovery, it is essential to explore the online purchasing fundamental’s operating shortfall and the possible factors that will influence consumer behaviour during this Covid-19 pandemic. Through the initial findings, the factors that appeared to influence Generation Z’s online purchase behaviour were security, price, risk, benefit, and e-commerce purchase experience.

THEORETICAL BACKGROUND

Icek Ajzen’s Theory of Planned Behavior (TPB) investigates how intention and behaviour are related. TPB explores three main elements (attitude, subjective norms, and perceived behaviour control) that affects certain behavioural intentions of individuals. TPB is used to analyse the factors that make human behavioural decisions (Ajzen, 1991). However, the purchase behaviour varies according to external conditions (Groening, Sarkis, & Zhu 2017). The inclusion of “perceived behaviour control” as one of the key variables makes TPB worth considering in this research. To make decisions in the context of the pandemic, TPB has served as the foundational theory for understanding consumers’ purchase intentions in the past for numerous studies (Daellenbach, Parkinson, & Krisjanous, 2018). Ali (2020); and Tien, Ngoc, and Anh, (2021) have confirmed in their study respectively that consumer behaviour has changed due to the pandemic.

Price

Zhao, Yao, Liu, and Yang, (2021) revealed product pricing is one of the variables that statistically significantly influences how buyers make decisions; pricing has a greater impact on consumer purchasing behaviour (Huo, Hameed, Sadiq, Albasher, & Alqahtani, 2021). Price can be judged using a variety of factors, including affordability, fairness, discounts, rival pricing, and price suitability (Kotler & Keller, 2012; Kusdiyah, 2012). Meanwhile, Neger, and Uddin, (2020) found that low-priced products lead to frequent purchases or large-volume purchases of products. Merchants who provide low-priced products have a competitive advantage because price-sensitive consumers will compare merchants and find sellers who provide low-priced products. However, factors like quality will also influence online purchase behaviour even if the price is higher than the price range (Vastani & Monroe, 2019).  In addition, past research found that consumers continue to spend wisely; shop locally and compare prices. Consumers are dissatisfied if they must pay more without improving the quality of products or services at the same time (Hunneman, 2020). Finding by Alonso (2021) indicated 51% of Generation Z believe that price comparison is their biggest online shopping advantage.

Therefore, hypothesis 1 (h1) suggested there is a relationship between price and online purchase behaviour among Generation Z in Kuala Lumpur and Selangor.

E-commerce Purchase Experience

Past e-commerce purchase experience has a strong correlation with future online purchase behaviour as previous experiences can reduce uncertainty and positively affect future online shopping intentions. For example, if someone has bought a product online and is satisfied with the merchant’s attitude and product, then this experience will affect their intention to repurchase in the future (Aziz & Wahid 2018; Isa et al., 2020). The expectation of products or services are formed based on past experience, customers can thus evaluate and compare perceived and expected service quality (Hassan, Nilufar, Fahad, 2020. Finding by Ji, Li, and Nie (2017) pointed out that ‘the good or bad past’ e-commerce experiences can influence consumer purchase behaviour in the future. Lots of consumers believe that the product and services are worth buying from the review provided by the merchants. Therefore, there is a positive correlation between past e-commerce experience and purchase intention (Hassan et al., 2020).

Therefore, hypothesis 2 (h2) suggested there is a relationship between e-commerce purchase experience and online purchase behaviour among Generation Z in Kuala Lumpur and Selangor.

Security

In e-commerce, the most important feature for customers is security. This includes personal, login and bank card information. Hossain, Yagamaran, Afrin, Limon, Nasiruzzaman, and Karim, (2018); Neger and Uddin (2020) showed that the most important element influencing consumers’ online shopping preferences is security. This is because consumers are hesitant to use online shopping due to insufficient security to protect their credit and debit cards from fraud or misuse. One of the most crucial security factors that should be taken into account when purchasing online is privacy (Bhatti & Ur Rehman 2019). Generally, consumer confidence in disclosing their personal information and paying by credit card directly affects consumers’ willingness to visit and purchase from online stores. However, a change in subsequent disclosure of online information was very unlikely. This means, then, that negative experiences may increase people’s concerns about online privacy, at least to some degree, but not necessarily change their behaviour (Masur & Trepte 2021). The misuse of tangible information, such as financial fraud or identity theft, is what is most frequently regarded as a privacy infringement (Chen & Atkin, 2020). Fearing that this personal information will be used unethically by illegal persons, is an important reason why consumers are reluctant to shop online. Privacy hazards significantly raise uncertainty and influence online shopping activity. Consumers’ lack of trust is a result of a lack of website privacy and security. This is one of the drawbacks of online buying that has kept it from growing. Therefore, it is imperative for the merchants to understand and strengthen the security of online transactions on its website, provide consumers with a privacy policy to improve consumer confidentiality, satisfaction, and purchase intentions. For consumers, the more secure the online shopping system, the more privacy is guaranteed, which can reduce transaction risks. Trust and confidence when shopping online can improve the shopping experience (Bhatti & Ur Rehman 2019).

Therefore, hypothesis 3 (h3) suggested there is a relationship between security and online purchase behaviour among Generation Z in Kuala Lumpur and Selangor.

Perceived Risk

Online purchasing behaviour is significantly impacted by perceived risk (Tran & Nguyen, 2022). Despite the growth possibilities for e-commerce in Malaysia, it is crucial to control consumers’ perceptions of risk to encourage more people to shop online (Goi, 2016). Purchase intention and risk perception are closely impacted by each other. Customers are less inclined to buy or repurchase online when they perceive high risks (Tham et al., 2019). Perceived time risk is related to wasted time. For instance, time wasted due to difficulty in the delivery, replacement, or repair of products as well as in the processing of transactions and information searches. In addition, the perceived delivery risk is also considered. Reference is made to inadequate delivery causing harm, while perceived after-sales risk is related to the potential harm caused by the difficulty of contacting sellers and consumers (Balogh & Mészáros, 2020). The return policy is one of the perceived risks that include the procedure for product returns, the product exchange policy, and the shipping cost of the product returned to the online merchant. The “Cash Return Guarantee” is the easiest way to process products online. The benefit of this strategy is that consumers can shop with confidence, enjoy the protection of the return policy, and provide buyers with the opportunity to return the product if the product does not meet the requirements. On the contrary, the drawback is that the return procedure could be more time-consuming or cost a little money for the item that is being returned (Tham et al., 2019). According to Tran and Nguyen (2022) online shopping involves more confusion and risk compared to traditional method. In their study, they showed that the most likely factor affecting consumers’ acceptance of online shopping is perceived risk (Ariffin, Mohan & Goh, 2018).

Therefore, hypothesis 4 (h4) suggested there is a relationship between perceived risk and online purchase behaviour among Generation Z in Kuala Lumpur and Selangor

Perceived Benefit

Perceived benefit is defined as consumers’ trust and satisfaction with online dealings, compared with traditional shopping, online shopping is convenient and simple, with more product varieties (Bhatti & Ur Rehman 2019). Product review in the era of online purchasing is considered a vital factor affecting online repurchase willingness, the perceived value of a consumer is affected by perceived quality, perceived competitive price, and the reputation of the website (Sullivan & Kim, 2018). Consumers always pay attention to high-quality products, and it has a significant impact on consumers’ online purchase behaviour. Online consumers can learn about real high-quality products by browsing reviews from other buyers, product photos and descriptions provided. According to Okeke (2019), if the perception of customers towards the product or service is better than expected they will be satisfied if the product or service meets expectations. On the contrary, if it is lower than expected, they will be dissatisfied. The study, therefore, concludes that the main predictors of online repurchase willingness is perceived benefit. The ease or convenience of online shopping is defined as one of the perceived benefits of online purchase behaviours. They have a positive impact on customers’ desire to use this shopping method, which has been summarized in many previous studies (Dost, Illyas, & Rehman, 2015; Bhatti & Ur Rehman 2019).

Therefore, hypothesis 5 (h5) suggested there is a relationship between perceived benefit and online purchase behaviour among Generation Z in Kuala Lumpur and Selangor.

Online Purchase Behaviour

Following the Covid-19 crisis, people have shifted to using virtual alternatives for their routine tasks and spending patterns, causing businesses to revise and reassess their strategy. Consumers being isolated at home have led to a substantial increase in online shopping (Stanciu, Radu, Sapira, Bratoveanu, & Florea, 2020). Online purchase behaviour is a vital perspective of consumer behaviours. The willingness of consumers to purchase over the internet is referred to as online consumption behaviour (Pena et al., 2020). Customers will develop online purchase behaviours after consideration of various reasons (price, e-commerce buying experience, security, perceived risk, and perceived benefit). In present time, researchers have not only conducted research on brick-and-mortar shopping behaviours, but also conducted research on purchasing behaviours in different marketing fields; for example, green-market, business-to-business (B2B) transactions, and online purchases (Peña et al., 2020). Past researchers have explored the reasons why consumers participate in online purchasing or the reason why they prefer traditional shopping (Trivedi & Yadav, 2020; Khwaja, Jusoh, & Md Nor, 2019); while the reason hindering online shopping comes from unforeseen changes (perceived risk) (Talwar, Dhir, Kaur, & Mäntymäki, 2020). Today, the online purchasing power of Generation Z cannot be underestimated. For this reason, merchants need to understand consumers’ shopping mentality, evaluation and other factors that ultimately generate purchase intentions in order to predict Generation Z’s online consumption behaviour. The online purchase intentions in this research are aligned with earlier studies that show consumers’ willingness to purchase products through online shops, which can also be understood as online purchase intentions.

Conceptual Framework

Price and perceived benefit are adopted from Triandewo and Sagy (2021), while e- commerce purchase experience, security and perceived risk are adopted from Ali, Amir, and Shamsi (2021). This research explores the selected variables that affect the online purchase behaviour among Generation Z in Kuala Lumpur and Selangor. There are five independent variables based on the relevant theoretical framework described in the previous section, namely price, e-commerce purchase experience, security, perceived risks, and perceived benefits; the dependent variable is the online purchase behaviours of Generation Z as shown in conceptual framework in Figure 1.

Fig 1. Conceptual framework of factors influencing online purchase behaviour among generation Z in Selangor and Kuala Lumpur.

RESEARCH METHODOLOGY

According to Ames, Glenton, and Lewin (2019) purposive sampling is defined as judgment sampling. It tends to target specific characteristics of target respondents who can contribute to relevant research. The main purpose of selecting purposive sampling is because it can generate a sample as a representative of the population as mentioned by Tjiptono, Khan, Yeong, and Kunchamboo (2020). The largest age group in Malaysia right now is Generation Z, which makes approximately 29% of the total population. This generation is largely reliant on social media and their cellphones, 8 hours daily on average spent online. As an electronically active generation, hence, they have higher purchasing power than other generations in online shopping (Haggerty (2020).

Data was gathered via a survey questionnaire that was distributed to target respondents using Google Forms. Several considerations were taken before distributing the questionnaire. This part as a screening question in this research to ensure that the survey subjects meet the requirements such as respondents belong to Generation Z, living in Kuala Lumpur and Selangor, and had online shopping experience before.

The sample size for this research was calculated using the Krejie and Morgan (1970) formula, with a requirement of 380 respondents. With that, to reach the required sample size of 380, 400 questionnaires were distributed for this research. For data analysis, the research involved 398 respondents after deleted 2 outliers with the use of the Statistical Package for the Social Sciences (SPSS).

RESULTS

KMO is statistical data that measures the adequacy of sampling. It shows the proportion of variable variance that may be caused by potential factors. KMO has a value that ranges from 0 to 1. According to Sekaran and Bougie (2016), the result of the factor loadings may be improper if the value is less than 0.60. Meanwhile, the correlation matrix hypothesis matrix is identified using Bartlett’s sphericity test. It can detect irrelevant and inappropriate variables. The data for factor analysis is appropriate if Bartlett’s significance threshold is less than 0.05. According to Table 1, the KMO’s value is 0.79, indicating that factor analysis is appropriate; the Bartlett test of approximate chi-square sphericity is 3634.07, with a significant value at 0.000, which is less than 0.05.

Table I Results of Kmo and Bartlett’s Test for Independent Variables of Study

Table 2 displays the dependent variable’s KMO and Bartlett results. The Bartlett test of approximate chi-square sphericity is 711.22 with significant value at 0.000, which is less than 0.05, and the KMO value of 0.8 indicates that factor analysis is adequate.

Table II Results of Kmo and Bartlett’s Test for Dependent Variable of Study

Table 3 reflects the interpretation results of the total variance of each component. The first component, the value is 2.435>1, the second component is 0.945<1, the third component is 0.856<1, the fourth component is 0.690<1, the fifth component is 0.586<1, and the sixth components are 0.488 <1. In addition, the percentage of extraction sums of squared loadings shows that this factor accounts for 40.589% of the observed variance characteristics. The complexity of the data set can be greatly decreased by using these components because it is account for almost 41% of the variability in the original six variables.

Table III Results of Total Variance Explained for Each Component

Higher Cronbach Alpha values indicate greater dependability for the variable (Sürücü, & Maşlakçı, 2020). According to Table 4, all reliability values are higher than 0.7, indicating that the questions are valid and reliable for this research. The Cronbach’s Alpha value for Price value is 0.754, E-commerce Purchase Experience is 0.804, Security is 0.720, Perceived Risk is 0.823, Perceived Benefit is 0.815, and Online Purchase Behaviours is 0.832. The findings lead to the conclusion that all dimensions in the questionnaire are consistent and stable between 0.720 and 0.832, which has good reliability.

Table IV. Result of Reliability Analysis (N=398)

The results of mean and standard deviation reflect the dominant tendency and variability of the data (Mertler & Reinhart, 2016). Referring to Table 5 below, all variables in this research confirmed a positive mean value such as price recorded with 3.8347, e-commerce purchase experience 3.7995, security 3.7628, perceived risk 3.7332, perceived benefit 3.8186, online purchase behaviour 3.7899. Similarly, the obtained standard deviations for all variables are positive and below 1, which demonstrates data variability (Sekaran & Bougie, 2010). Therefore, price recorded .77160, e-commerce purchase experience .86318, security .73215, perceived risk .87866, perceived benefit .86146, online purchase behaviour .88109.

Table V. Descriptive Statistics

According to Kline (2011), when the value of skewness is ±3 while the value of kurtosis is ±10, the normal distribution of the constructed term can be obtained. Table 6 showed the skewness and kurtosis values for all variables. The price recorded skewness value of -1.915, e-commerce purchase experience recorded -1.703, security recorded -1.734, perceived risk recorded -1.482, perceived benefit recorded -1.714, and online purchase behaviour recorded -1.621. Meanwhile for kurtosis, price recorded 4.015, e-commerce purchase experience recorded 2.949, security recorded 3.881, perceived risk recorded 1.766, perceived benefit recorded 2.996, and online purchase behaviour recorded 2.544. In this research, all the skewness values of the construct are between -1.915 and -1.482, and the kurtosis values of the construct are between 1.766 and 4.015. All values for skewness and kurtosis are within acceptable range, indicating that all items are normally distributed.

Table VI. Results of Skewness and Kurtosis

The impact of collinearity between variables in a regression model is measured using the variance inflation factor (VIF). According to Daoud (2017), as a potential collinearity problem, the tolerance value should be 0.20 or lower, and the VIF value should be greater than 5 to be recognized. Based on Table 7, the values ​​of tolerance for price recorded 0.796, e-commerce purchase experience 0.799, security 0.686, perceived risk 0.811, and perceived benefit 0.727. Meanwhile VIF value for price recorded 1.257, e-commerce purchase experience 1.251, security 1.458, perceived risk 1.233, and perceived benefit 1.375. The tolerance and VIF value for all independent variables have exceeded the critical value ​​of 0.20 and 5. Therefore, all variables in this research do not have any problem of multicollinearity.

Table VII Results of Collinearity Statistics

In this research, Pearson correlation was used to determine the significance and direction of the link between two variables (Pallant, 2001) and to testify how well the measure’s results align with the conceptual framework that has been constructed regarding the survey’s questionnaire items (Sekaran & Bougie, 2016). According to Cohen (1988), the relationship can be classified into several categories, namely weak relationship (r = 0.10 to 0.29 or r = -0.10 to -0.29), moderate relationship (r = 0.30 to 0.49 or r = -0.30 to -0.49), and strong relationship (r = 0.50 to 1.00 or r = -0.50 to -1.00), based on the r-value. Table 8 showed the value of correlation coefficient for price toward online purchase behaviour recorded 0.289, e-commerce purchase experience toward online purchase behaviour recorded .158, security toward online purchase behaviour recorded .136, Perceived Risk toward online purchase behaviour recorded .143, and perceived benefit toward online purchase behaviour recorded .280.

Table VIII Results of Pearson Correlation Analysis

To assess the strength of the association between the outcomes of online purchase behaviour (dependent variable) between multiple predictor variables, and the importance of each predictor variable to the relationship, multiple regression analysis is utilized. If the P-value is less than 0.05 (p<0.05), the relationship is significant, and vice versa. Based on Table 9 the price and perceived benefit is significant at 0.000, which is below the significance level of 0.05. Therefore, there is a positive relationship between price toward online purchase behaviours and perceived benefit toward online purchase behaviours. However, the e-commerce purchase experience significance value recorded 0.794; the security recorded 0.266 and the perceived risk recorded 0.396. Due to the significance of the e-commerce purchase experience, security and perceived risk all exceed the threshold of 0.05 p-value; therefore, there is no positive relationship between any of these and online purchase behaviour.

The R² is 0.128, indicating that price, e-commerce purchase experience, security, perceived risk and perceived benefit account for 12.8% of the variation of online purchase behaviour. Referring to Kumari and Yadav (2018), the Durbin-Watson value should be close to 2 to indicate that there is no autocorrelation error term problem. Meanwhile, the Durbin-Watson value for this research is 1.364 which confirms non-autocorrelation. The result of the ANOVA showed F statistic value is 11.462 > F, and the significance P = 0.000 < 0.05, which means the Price, E-commerce Purchase Experience, Security, Perceived Risk and Perceived Benefits (all independent variables) have a significant impact on Online Purchase Behaviour (dependent variables).

Table Ix Results of Multiple Regression Test

DISCUSSION

This research’s aim is to determine whether the factors (1) Price (h1), (2) E-commerce Purchase Experience (h2), (3) Security (h3), (4) Perceived Risk (h4), and (5) Perceived Benefit (h5), have influence online purchase behaviour among Generation Z in Kuala Lumpur and Selangor. According to the multiple regression

analysis results, it shows that (h1) and (h5) significantly positive influenced online purchase behaviour among Generation Z, however, (h2), (h3), and (h4) had rejected as there were no significant effects.

Price on Online Purchase Behaviour

Price (h1) is one of the elements that Generation Z considers while making online purchases. The hypothesis (h1) was supported, the result showed there are significant positive relationships between price and online purchase behaviour. The study’s findings are consistent with earlier ones from Jadhav and Khanna (2016); Zhao, Yao, Liu and Yang (2021); Roy, and Datta, (2022) acknowledged price was the key determinant of online purchasing. The factors that attract Generation Z for online purchase is i.e., easy price comparison, cost effective, better discount and reasonable fees.  Therefore, it is essential for online shopping platforms to focus on product prices that satisfies their customers. This finding od this research is in consensus with Neger and Uddin (2020). Therefore, this research concludes there is a positive relationship between price and online purchase behaviours among Generation Z.

E-commerce Purchase Experience on Online Purchase Behaviour

One of the other elements influencing Generation Z’s online purchasing behaviour is E-commerce Purchase Experience (h2). The results of this research showed there is no significant relationship between e-commerce purchase experience on online purchase behaviour. The result contradicts with Isa et al., (2020), online purchase experience increased the online purchase tendency of consumers. Meanwhile Daroch, Nagrath, and Gupta (2020) revealed that people avoid online purchase because they lack knowledge and had bad experiences in the past. However, Risca (2019) indicates e-commerce purchase experience was vital however it does not influence Generation Z’s purchasing behaviour because online purchase has become a norm amongst the Generation Z. Generation Z places less importance on past experienced regardless of it is good or bad. This is because for Generation Z the most influential factor is the technologies (Priporas, Stylos, & Fotiadis, 2017). Johan, Md. Syed, and Adnan (2022) revealed that smart technologies significantly affect the experiences of generation Z consumers, and they anticipate that the technology will help them make better purchasing decisions. Therefore, this research concludes there is no positive relationship between experience and online purchase behaviours among Generation Z.

Security on Online Purchase Behaviour

In this research Security (h3) is considered as one of the factors that influence online purchase behaviour among Generation Z. However, the study found security has no significant relationship with online purchase behaviour among Generation Z.  Past study (Hossain et al., 2018; Neger and Uddin, 2020) revealed security factor has a significant and positive association with consumers’ online shopping behaviour however, this research found there is no association between security and online purchase behaviour. The findings of this research are similar to Irawan (2018) wherein, consumers’ decisions for online purchase are negatively and less affected by security. Generation Z who are internet savvy with high computer literacy (Aldhmour & Sarayrah, 2016; Isa et al., 2020) believe, their personal data are not exposed and disclosed to third party and personal privacy are protected by website security itself while doing the online shopping. Due to Malaysian banks’ stringent authentication procedures for online card transactions, Generation Z members feel comfortable making purchases online (Bank Negara Malaysia (BNM),2022, in Free Malaysia Today). According to Bhatti and Ur Rehman (2019), the more secure the online shopping system, the more privacy is guaranteed for customers. This suggests the reason as to why security factor does not influence Generation Z online purchase behaviour.

Perceived Risk on Online Purchase Behaviour

In this research perceived risk (h4) is rejected, the findings showed there is no significant relationship between perceived risk and online purchase behaviour. The findings correspond with past study on perceived risk (Ariffin, Mohan, & Goh, 2018; Nuzula, & Wahyudi, 2022). Generally, Generation Z pay less attention to the potential risks as they believe it is difficult to judge

the product quality through the online. However, if there are any problems with the order, customers can ask for a refund because the dependability of online resources depends on the acceptance of orders, prompt delivery, and rapid response (Tham et al., 2019). Therefore, internet shoppers are less technically vulnerable compared to direct shopping (Tham et al., 2019). The result from Ventre and Kolbe (2020) indicates that knowledgeable consumers do not let perceived risk affect their purchase behaviour. This suggests the reason why perceived risk does not influence Generation Z online purchase behaviour.

Perceived Benefit on Online Purchase Behaviour

In this research perceived Benefit (h5) was supported, the outcome showed there is significant relationship between perceived benefit and online purchase behaviour. The outcome of this research aligned with Bhatti and Ur Rehman (2019). It is evident that Generation Z prefers to purchase online because it is easy and convenient. Additionally, people frequently shop online because they can access the website and the online store at any time and find what they need (24 hours a day, 7 days a week) (Dabija & Lung, 2019). Some of the perceived advantages of online purchases include the simplicity and convenience of using online buying services. Consequently, there is a positive relationship between perceived benefit and online purchase behaviour among Generation Z.

CONCLUSION

In conclusion the factors i.e. price and perceived benefits have a significant relationship with online shopping behaviour between generation-z who stayed in Kuala Lumpur and Selangor. Price was the most significant factor (β = .230; p = .000 < .05) and followed by perceived benefit (β = .218; p = .000 < .05). Price was ranked first because it is important for generation-z to make a comparison of prices of the product they want; online shopping platforms charge reasonable prices; and for them, purchases online are more money-saving than purchases at the outlet. Meanwhile, for perceived benefit, generation-z prefers to purchase online because it is easier compared to traditional shopping. For example, generation-z can shop at any time; easily get detailed product information from the buyers; buy a wider range of product selections; and avoid the chaos of traffic and crowds in the market.

However, factors such as e-commerce purchase experience, security, and perceived risk are found to be less important in the online purchase behaviour of generation-z. For example, e-commerce purchase experience, positive or negative experiences; feedback; and recommendations of other buyers do not affect generation-z purchases online. Security, generation-z is less confident during online shopping because of their mind block with the assumption that the bank card details may not protected; that personal information provided may not be disclosed to third parties; and that website security may not be guaranteed. Perceived risk, generation-z perception of online shopping may be receiving the wrong and faulty product ordered through online shopping; difficulty in judging the product quality; and the probability do not receive a refund if there is any problem with the product. Therefore, the findings of this research should benefit those in academia and industry to deepen understanding about online shopping behaviour among generation-z since most of them the larger population who are doing online transaction which will contribute to the economic growth in Malaysia by 2030. Even so, there are two limitations and recommendations of this research Firstly, this research exclusively focused on generation-z who stayed in Kuala Lumpur and Selangor. This research recommends explore other state such as Pahang, Sabah, Perak, Johor to represent Malaysia. Secondly, this research only focusses on direct relationship between independent variables toward dependent variable. It is recommended to add indirect relationship such as moderator or mediator to assess the relationship.

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