Consumers’ Behavior in Online Purchasing among Universiti Sultan Zainal Abidin (UniSZA) Undergraduates Students

Consumers’ Behavior in Online Purchasing among Universiti Sultan Zainal Abidin (UniSZA) Undergraduates Students

Nursyakirah Sulaiman¹, Nalini Arumugam²

¹Faculty of Bioresources and Food Industry, Universiti Sultan Zainal Adibin, Besut Campus, 22200 Besut, Terengganu, Malaysia.

²Faculty of General Studies and Continuing Education, Universiti Sultan Zainal Adibin, Gong Badak Campus, 21300 Kuala Terengganu, Terengganu, Malayia.

DOI: https://dx.doi.org/10.47772/IJRISS.2025.90400490

Received: 13 April 2025; Accepted: 23 April 2025; Published: 23 May 2025

ABSTRACT

Online purchasing is an act for purchasing goods and services using internet. Online shopping has become more and more popular as the internet has grown. In online purchasing, consumers’ behavior is critical for understanding the psychological and behavioral factors that influence people’s purchasing decisions, consumer more concern about security, privacy and customer loyalty and there are significant research gap in characteristics influencing online shopping behavior among undergraduates, still lack on specific characteristics and preferences influencing online purchasing decision making among undergraduates. This research used Theory Planned behavior. Objectives for this research is to determine the level of consumers’ behavior in online purchasing among UniSZA undergraduate, Specifically, the research aim to identify the factor that influence consumers’ behavior in online purchasing among UniSZA undergraduates and to identify the relationship between attitude, trustworthiness, loyalty, security and safety, convenience and consumers’ behavior in online purchasing. This research have been employed quantitative method, which conduct survey on 210 UniSZA undergraduates students through provide questionnaire that shows strong reliability in pilot study. Data analysis utilized IBM SPSS, employing Descriptive Analysis, Pearson – Correlation Analysis and Multiple Regression Analysis for interpretation. In Pearson – Correlation Analysis attitude have very strong correlation value with 0.812, while in Regression Analysis all variables have positive relationship with consumers’ behavior in online purchasing with significant value less than 0.05. These findings help to gain a comprehensive view of undergraduates decision when engaging in online purchasing. The findings also help in having more accurate data about wishes of consumers’ in choosing to purchase online.

Keywords: Online purchasing; Consumer behavior; Undergraduates; Attitude; Trustworthiness, Loyalty; Security and safety; Convenience; Theory Planned behavior

INTRODUCTION

Online shopping is an act for people purchasing and shopping for some products and services using internet connections. Online shopping has become more and more popular as the internet has grown. Internet users use a wide range of purchasing systems, apps and user interfaces, some of which differ significantly in terms of their intended usage. Despite this diversity, everyone forms a generalized perception of the online shopping experience. International Data Corporation (IDC) reports that global consumer expenditure on internet purchases increased from $118 billion in 2001 to $707 billion in 2005 (Wolverton, 2001). Revenue from e-businesses increased from 4% of global company sales in 2000 to 7% in 2001. According to the same scenario, Malaysians must recognize these tendencies and move quickly to participate actively in the rapidly developing electronic world.

The association between purchase intention and actual consumer behavior has been scientifically established in the context of internet purchases (Ajzen & Driver, 1992; Ajzen & Fishbein, 1980). The popularity of internet purchasing in purchase intention has been shown in previous studies (Nejati et al., 2011; Zhao et al., 2017). According to Mainardes et al., (2019), online purchasing intention is a factor that predicts real consumers’ behavior in an attempt to accomplish an online purchase transaction using a website. Another way to characterize an online shopping intention is a customer’s future plans to make purchases online (Pavlou, 2003). Online purchase intention was defined by Shaouf, Lu, and Li (2016) as the circumstance in which a customer plans to make an online purchase.

The phases of consumers’ decision-making process were also involved in online consumers’ behavior, such as problem identification like recognizing a problem with consumption, information search for looking for information to solve the problem, evaluation (i.e., determine the probability of an outcome or event), choice, selecting which products to buy), and outcomes (i.e., feeling satisfied or dissatisfied with the product, or discarding the product) (Darley et al., 2010). In the context of internet shopping, consumers frequently exhibit their inconsistent behaviors.

Consumers use the Internet for a variety of reasons and intentions in the business-to-consumer (B2C) e-commerce cycle activity. These include looking for product features, prices, or reviews; choosing products and services online; placing an order; paying for the order; and using any other method. The delivery of the necessary products is then handled by the Internet or another method; and finally, sales service is provided through the Internet or another method (Sinha, 2010). The Internet has become a widely used medium in many industrialized nations, providing a vast range of items with round-the-clock access and extensive coverage. In business transactions, e-commerce has also emerged as an indispensable marketing channel. In B2C transactions, online retailers and services play a significant role as sales channels. Over the past ten years, one of the most significant research themes in e-commerce has been the study of consumer online purchasing behaviors (Chen, 2009). Information systems, marketing, management science, psychology, social psychology, and other fields have all studied online consumers’ behavior (Hoffman and Novak, 1996; Koufaris, 2002; Gefen et al., 2003; Pavlou, 2003, 2006; Cheung et al., 2005; Zhou et al., 2007).

The act of making purchases of goods or services over an Internet can refer to online shopping behavior (also termed internet buying behavior and Internet shopping/buying behavior). There are five steps process, which are comparable to those in traditional shopping behavior (Liang and Lai, 2000). Besides that, they will assess the options and select the one that most closely near their requirements for satisfying the felt desire. Ultimately, a sale is made and after-sale services are rendered. The term “online purchasing attitude” describes how people feel about making purchases on the Internet (Li and Zhang, 2002). The study analyzed consumer behavior in two distinct domains: information seeking and purchasing. The two domains were impacted by various factors such as perceived risk and trust, the consumer’s attitude, social influence, personal online skills, and technology-related aspects like website features and perceived usefulness. Furthermore, studies in the past have demonstrated that shopping orientations, demographics, perceived channel utility, and channel knowledge all influence online purchasing behavior (Li, Cheng, and Russell, 1999; Weiss, 2001).

Online shopping has become more common in today’s digital era, influencing consumer behavior and tastes. Understanding the dynamics of customer behavior in the context of online transactions is critical for businesses looking to engage with their target audience and increase sales. Consumers’ online purchasing decisions are influenced by a variety of factors, including trust in online retailers (Jarvenpaa et al., 2006), the impact of online reviews on purchasing behaviors (Mo et al., 2015), and the function of online brand trust (Kharel, 2019).

Consumer trust is important in online purchasing decisions, with studies highlighting elements such as brand familiarity and reputation (Rahman & Mannan, 2018). Furthermore, the transparency of product information, as well as the perceived benefits and risks of online buying, can influence consumer behavior (Fu et al., 2022; Bhatti and Rehman, 2020). The impact of online communities, electronic word of mouth, and brand image for online purchasing behavior emphasizes the interconnectedness of online consumer behavior (Stefanny et al., 2022).

Theory of Planned Behavior

Theory of Planned Behavior (TPB) has stated an individual’s desire to do a specific behavior determines whether or not he or she does it. Intent is influenced by attitudes toward the conduct and views about the individual’s or people’s ability to be successful and engage in their target behavior. Azjen (1985) said that an attitude toward an action as a favorable or negative assessment of executing that behavior. Attitudes are informed by beliefs, and perceived behavioral control is informed by beliefs about the individual’s access to the opportunities and resources required to engage in the behavior (Azjen, 1991). Azjen (1997) connects perceived behavioral control to Bandura’s idea of perceived self-efficacy. TPB also establishes a direct relationship between perceived behavioral control and behavioral achievement.

The current study’s fundamental concept is that opinions regarding Internet privacy and trustworthiness influence attitudes toward Internet purchasing. TPB will provide a solid theoretical foundation for testing such a premise, as well as a framework for determining if attitudes are related to intent to engage in a specific conduct, which in turn should be related to the actual behavior. According to the hypothesis, thoughts about how important referent others see Internet shopping, as well as motivation to conform to the opinions of important others, should impact intent to make Internet purchases. Finally, views about having the requisite opportunities and resources to engage in Internet purchases should influence both intent to purchase and actual purchasing behavior.

Impact of online purchasing

Purchasing behavior has unique and distinct activity that directly represents consumers’ wants, needs, and pursuit of material and spiritual interests (Braithwaite and Scott, 1990). Cici and Bilginer Ozsaatcı (2021) identify social, cultural, demographic, and environmental elements that influence buying behavior. As a result, the COVID-19 pandemic, as a social factor, influences various changes in purchasing behavior. Scholars generally assume that a high number of consumers engaged in panic or impulsive buying behavior during the early stages of the COVID-19 pandemic (Aljanabi, 2021; Stuart et al., 2021), which was accompanied by compulsive purchase behavior (Samet and Gozde, 2021). Purchase behavior during the COVID-19 pandemic is characterized by mobility (Gao et al., 2020; Zhang et al., 2020; Lu et al., 2021). The use of digital technology has produced ideal conditions for customers to engage in online purchasing, and online purchase activities have expanded dramatically (Jiang and Nikolaos, 2021). However, the changes in purchase behavior described in the preceding research focus on a single variable and do not systematically sort out the changes in consumer buying behavior during the COVID-19 epidemic.

According to Ngugi (2014), while online shopping has grown rapidly in the developed world, it has yet to catch on in developing countries such as Kenya. This is an excellent niche for firms looking to establish themselves online. However, many organizations in Kenya are still reluctant and doubt the benefits of online presence. This is due to greater competition for consumers’ attention online. Today’s consumers have become part-time marketers. They understand marketing and expect firms to be honest. Notably, most customers are still concerned about losing money through exploitative sales and credit/debit card fraud. Consumers have perceived risks that influence their attitude, and their past experiences influence their purchasing behaviour.

Consumers’ Behavior in online purchasing

Kotler and Keller (2009) define consumers’ behavior as research of how individuals, groups decide on what they need, want and how to obtain it. The stimulus and response model explains how buyer behavior is influenced by marketing stimulus like price, place, and promotion, as well as environmental factors like economics, technology, politics, and culture. Consumer nature and decision-making processes, such as problem detection, information search, evaluation, and post-purchase behavior, influence product selection, brand choice, dealer selection, purchase time, and number of purchases. According to the buyer behavior model by Kotler and Armstrong (2010), Research highlights the significance of understanding how consumers’ characteristics, as well as cultural, social, personal, and psychological factors, influence their purchasing behavior and interest in products and services.

Consumers’ behavior can be state as the process that people, groups, and organizations choose, organize, and purchase products, services, experiences, or ideas to meet their needs and desires, as well as the effects of these activities on the consumer and society. (Kuester, 2012) Consumer behavior varies depending on the purchasing decision, which is influenced by purchasing opinions and habits, which are then twisted by psychological and social variables that influence purchase decisions. Online purchasing consumer behavior is not limited to online shopping. They also use the internet to locate and compare pricing for products, services, and news. The recession has a significant impact on internet consumer behavior. The behavior of an online purchasing customer is determined by a variety of factors, including shopping goals, personality, understanding of ecommerce, motivations, incentives, and experience. Today, numerous organizations have entered the online arena to capitalize on its tremendous potential. Players such as Flipkart, Amazon, e-Bay, and Shopclus are particularly active in this space. These corporations are particularly aggressive in promoting their brand to the youthful demographic by delivering convenience, variety, better cost, and quick disposal of the product. (Upasna, 2012).

Study Conceptual Framework

This figure shows the conceptual framework that using in this study. The Dependent Variable (DV) for this research is Consumers’ behavior in online purchasing while Independent Variable (IV) is Attitude, Trustworthiness, loyalty, Security and safety, convenience. First the study will focus on factor that influence consumers’ behavior in online purchasing. Second, followed by identify the relationship between Attitude, Trustworthiness, loyalty, Security and safety, convenience and consumers’ behavior in online purchasing.

Figure 1: Conceptual Framework

MATERIALS AND METHODS

Research design and study area

The primary data questionnaire will be delivered to UniSZA undergraduate students using a rigorous sampling approach. The sampling approach will include respondent from various faculties and programs to ensure a representative sample of the student population. Students will be classified using stratified random sampling based on their degree programs and ensuring that all relevant demographics are represented proportionally using quantitative method by conducting a survey of undergraduates to obtain accurate data with close – ended questionnaire. This strategy improves the generalizability of the findings while reducing sampling bias. Prior to distribution, the questionnaire will be pre-tested with a small sample of students to identify and correct any ambiguities or flaws in the questions, hence increasing the reliability and validity of the data collected.

Population and Sampling Method

In this research study, the population is the total number of undergraduates studying in three campuses of UniSZA, including Gong Badak, Besut, and Medical. Target respondent for this research is 210 respondents.

The research undertaken at three UniSZA campuses such as Gong Badak, Besut, and Medical campus and the researcher will focus on degree undergraduate students. The current population of undergraduate students for Degree programs at UniSZA Gong Badak Campus is 7140 students, Besut Campus is 1972 students, and Medical Campus is 584 students. The total population for Degree program are 9696 students.

The study’s sample methodology will be decided according to the following criteria: Initially, the selection process will involve choosing respondents from the programs offered by each faculty across the three locations at UniSZA. Gong Badak Campus comprises 7 faculties, consist of FSSG, FPP, FRIT, FKI, FUHA, FSK, and FBK. Furthermore, Besut Campus has 3 faculties, consist of FBIM, FIK, and Pharmacy. On the other hand, the Medical Campus accommodates a single faculty which is FP. Every faculty possesses a different quantity of programs. There are a total of 42 programs listed across 3 campuses for Degree courses. Furthermore, a total of 5 participants will be selected at random from each program within each faculty to complete the questionnaire. In Addition, the chosen participants must possess a propensity for engaging in taking part and derive pleasure from doing so.

Data Collection

In this study, a questionnaire was utilized as the primary approach to gather primary data, which was distributed face-to-face to specifically targeted respondents from the selected group.

Before the questionnaire was distributed, respondents were given a brief overview of the study’s goal to help them answer the questionnaire more accurately. Meanwhile, steps have been made to ensure the respondents’ confidentiality, including keeping their responses anonymous and securely protected. The questionnaire was carefully prepared to gather essential data in order to achieve the study’s aims. This questionnaire comprises of structured questions that allow respondents to provide their answers by selecting one of the answer alternatives provided, or by providing a written answer explanation. Participants were also informed of the estimate time needed to complete the questionnaire. The average completion time is around 10-15 minutes, so they are aware of the time commitment required. The questionnaire was also delivered in English and distributed to the respondents.

Reliability Test

The number of test items, item interrelatedness and dimensionality affect the value of alpha. (Cortina J) There are different reports about the acceptable values of alpha, ranging from 0.70 to 0.95. (Nunnally J, Bernstein L, 1994) A low value of alpha could be due to a low number of questions, poor interrelatedness between items or heterogeneous constructs. For example if a low alpha is due to poor correlation between items then some should be revised or discarded.

Table 1: Reliability value of Dependent variable and Independent variables

The purpose of reliability test is to determine the items reliability and internal consistency before proceed the field study. The reliability conducted with giving pre-test questionnaire to verify the content be reliable and valid whether the content and statement provided can be understood according the purpose of the questionnaire. This reliability conducted using 30 respondents for answer the pre-test.

Nunnally (1978) recommended that the minimally acceptable reliability for preliminary research should be in the range of 0.5 to 0.6, whereas in 1978 he increased the recommended level to 0 .7. In order to verify the validity of the survey instrument, the students’ questionnaire II is examined to make sure the survey items’ content is appropriate and aligns with the study’s goals. For content structures to be accurate and clear, a checking specialist is required (Kline, 2005; and Hulse, 2006). Gay & Air Asian (2003) state that in order to make sure that the instrument can satisfy the goals of researchers based on the established objectives, expert review and validation are required.

Data Analysis

The statistical analysis used for this study was in line with the research objective and include Descriptive Analysis, Pearson Correlation Analysis, and Multiple Regression Analysis. The researcher used a quantitative research approach to determine the level of consumers’ behavior in online purchasing and identify the relationship between attitude, trustworthiness, loyalty, security and safety, convenience and consumers’ behavior in online purchasing among UniSZA Undergraduates. Tool using for running quantitative research in this study is face-to-face questionnaires method that can interact directly with target respondent.

The study’s data was analyzed using Statistical Package for Social Sciences (SPSS) software. This tool helps you explore different outcomes by providing an overview of the link between independent and dependent variables. SPSS offers an easy-to-use interface for researchers and analysts to perform many statistical procedures. The dependent variable (DV) refers to the variable tested or evaluated in the experiment. Variables are largely used for analysis and manipulation by researchers, while the independent variable (IV) influences the dependent variable.

RESULTS AND DISCUSSION

Demographic Profile

The demographic background from 210 respondents displayed in table 1 shows that the majority of respondents that attend the survey conducted were female with 154 respondents (73.3 %) compared to 56 male respondents with (26.7%). From separated three groups such as 18-21 years old has 74 respondents, which is (35.2% ) of the total, the majority of respondents were 132 respondents (62.9%), from the age group between 22 and 25 years old and only four respondents are beyond the age of 26 years old, which is (1.9 %) of the total. . The majority of students 122 respondents (58.1%) rely on loans, such as PTPTN, to support their school or living expenses. Then, 124 respondents (59%) spending between RM 100 – RM 399. This shows that most of the participants have moderate financial ability, common for students or individuals with low income sources. Year 3 students being the largest group consisting of 83 respondents (39.5%), this shows that most participants are in semester 5 and 6 in their degree program while smallest group, Year 4 students, consisted of 19 respondents (9%), which may reflect whether fewer final year students answered the survey questions that were distributed. Across the different faculties, Faculty of Bioresources and Food Industry (FBIM) leads with 40 respondents (19%) followed by Faculty of Computer Science and Information Technology (FKI) with 30 respondents (14.3%). other faculties, such as Faculty of Business and Management (FPP), Faculty of Innovative Design and Technology (FRIT), and Faculty of Health Science (FSK), each representing 20 respondents (9.5%). Smaller faculties such as Faculty of Applied Social Sciences (FSSG) and Faculty of Languages and Communication (FBK) accounted for 15 respondents (7.1%) each, while Faculty of Law and International Relations (FUHA) represented 10 respondents (4.8%). The faculty least represented by respondents was Pharmacy (FF) with only five respondents (2.4%).

Table 2 : Demographic Profile of Respondent (n = 210 )

Level of Consumers’ behavior in online purchasing among UniSZA undergraduates’ students

Table 3: Level of consumers’ behaviour in online purchasing among UniSZA undergraduates

Researcher used descriptive analysis in order to determine the level of consumers’ behavior in online purchasing and measure five variable for factor influence consumers’ behavior in online purchasing among UniSZA Undergraduates students. Among All question asked, the findings indicate that UniSZA students place a high value on price comparison while shopping online. The mean of 4.41 shows a strong tendency to engage in this type of behavior among 119 respondents with (56.7%) are strongly agreed with this statement. This high percentage demonstrates a strategic and purposeful approach to online purchasing, in which students actively search for the best offers by comparing costs across different platforms. Only four respondents (1.9%) strongly disagreed, indicating that price comparison is the most common behavior among respondents. This tendency may be motivated by a desire to maximize value, particularly given the accessibility of price comparison via internet tools and shopping websites.

Pearson Correlation Analysis

Pearson Correlation Analysis was used to assess the strength of the relationship between independent and dependent variables. A Pearson-Correlation was used to measure the strength of the association between five variables in a linear equation. The coefficient, which ranges from -1 to +1, represents the strength of the association while the signs indicate its direction.

Table 4: Pearson – correlation

Table above demonstrates a positive relationship between various factors such as Attitude, Trustworthiness, Loyalty, Security and Safety, Convenience and consumers’ behavior in online purchasing. Among them, Attitude has the strongest positive correlation (r = 0.812), showing that a positive attitude greatly enhances online shopping behavior. Similarly, trustworthiness (r = 0.675), convenience (r = 0.638), and loyalty (r = 0.626) show strong positive relationships, indicating that these variables have a significant role in consumers’ participation in online buying. While, Security and safety shows a moderate positive relationship (r = 0.558), emphasizing their significance in establishing confidence in online transactions. The consistent positive relationship across all factors show that changing these elements can significantly improve customers’ online shopping behavior.

Martinez Lopez et al., (2005) state that an effective online purchasing strategy involves understanding customer behavior, beliefs, and attitudes, as consumers are actively involved in value creation. However, a thorough understanding of consumer behavior and attitude in online buying can reduce the possibility of substitution while providing long-term profitability (Wen, 2009). In the context of online purchasing, attitudes are especially important to measure because they have a favorable impact on online shopping intention and are regarded the most influential element. Furthermore, considerable empirical data suggests that online customers’ opinions positively influence their willingness towards looking for online products and price information.

Table 5: Model Summary multiple regression analysis

Table 4 shows R and R² values, which indicate the strength of relationship between independent factors such as attitude, trustworthiness, loyalty, security and safety, convenience and dependent variable Consumers’ behavior in online purchasing. In this context, the R-Value for attitude on consumers’ behavior is 0.812, 0.675 for trustworthiness, 0.626 for loyalty, 0.558 for security and safety, for convenience is 0.638. The data show a strong relationship, leading to the conclusion that theoretical framework is acceptable. The R² value for attitude, trustworthiness, loyalty, security and safety, finally for convenience control are 0.660, 0.455, 0.339, 0.312 and 0.407, respectively. This means that all independent variables; attitude, trustworthiness, loyalty, security and safety, convenience, together explain for 66.0%, 45.5%, 33.9%, 31.2% and 40.7% of variance. This shows the fact that Attitude is the most important predictor, other variables like Trustworthiness and Convenience also give moderate predictive ability, despite Security and Safety contribute the least.

Table 5 illustrates significant values for variable’s attitude, trustworthiness, loyalty, security and safety, convenience, with all values found to be <.001, respectively, which is less than 0.05 and considered acceptable. Therefore, attitude, trustworthiness, loyalty, security and safety, convenience indicates a significant relationship with Consumers’ behavior in online purchasing.

Table 6: Coefficient of multiple regression analysis

The unstandardized B coefficients, representing positive values, shows positive relationships. As attitude (0.767) the highest, trustworthiness (0.583) decreases, loyalty (0.565) decreases, security and safety (0.577) increases and convenience (0.620) increases, consumers’ behavior are expected to be increase, according to general equation. This statement explains the significant results from the thesis. Y= β₀ + β₁X₁ + β₂X₂ + β₃X₃ + β₄X₄ + β₅X₅.

Where;

Y – Consumers’ behavior in online purchasing

X₁ – Attitude

X₂ – Trustworthiness

X₃ – Loyalty

X₄ – Security and Safety

X₅ – Convenience

Thus, the equation for this study is:

Y = 0.604 + (0.767) X₁ + (0.583) X₂ + (0.565) X₃ + (0.577) X₄ + (0.620) X₅

As a results, the coefficients for all variables were statistically significant because the p – value was smaller than 0.05.

Figure 2: Plot graph

This plot shows if the differences between actual and predicted values in the regression model called residuals follow a normal trend. The straight diagonal line represents a perfectly normal pattern, whereas the dots represent actual data. If the dots remain closer to the line, as in this graph, it indicates that the residuals are mainly normal. This is a favorable indicator since it indicates that the regression model performs well and satisfies one of the major requirements for this type of study. Small variations at the end are usual and not a major concern. By looking at this graph it can be said that most students normally accept online purchases in their purchasing behavior although there are many students who are still worried about online purchases.

DISCUSSION

According to this study, most undergraduate students at three UniSZA campuses prefer to shop online more than traditional. Objectives of this study can summarize that online purchasing influence more student to purchase personal items. It was discovered that respondents’ attitudes play a significant effect in their willingness to shop online. This is supported by data showing that most students compare prices from several online businesses before making a purchase. In addition, most students stated that they will take advantage of online discount and promotion. With this let to know that many younger generation are tend to purchase online due to it is easy than traditional shop.

Attitude factor shows that students have a good perspective about online purchasing. They enjoy browsing online platforms and actively comparing prices before making purchases, indicating an engaged informed consumers’ approach. Many students prefer personalized suggestions based on previous purchases because it improves their buying experience. This implies that students are not just casual customers but rather take an organized strategy to their online purchasing, looking for the greatest discounts and opportunities to save money. Their excitement of discovering new online retailers and platforms reveals a sense of interest and willingness to new buying experiences. This shows that online stores that provide dynamic and individualized purchasing experiences are more likely to attract and retain students as customers.

Trustworthiness is another important factor that influences UniSZA undergraduates’ online shopping behavior. Students have a general trust in online retailers, particularly those who are upfront about shipping charges and delivery times. However, they are still concerned about the accuracy of product descriptions, availability, and whether the products match what is promoted online. This shows that, while students are willing to trust established online platforms, they still need clear and trustworthy information from shops in order to fully participate in the shopping process. Then, Loyalty has an interesting influence in students’ purchasing decisions. While students frequently return to websites with which they have had great previous experiences, they are also heavily impacted by price sensitivity. This means that even if a shop has established a relationship with a student, the desire for better prices and offers could cause to look into other choices.

When it comes to online buying, security and safety are top priorities, and students are afraid about sharing important information. Most students are confident in the security measures used by online stores, but they still carefully check security aspects before making a purchase. After that, Convenience is a significant factor in UniSZA undergraduates’ online shopping activity. Students admire the flexibility to shop at any time and from any location, eliminating the need to travel to physical establishments. This is especially significant for busy students who have little time for shopping and prefer to use online platforms that provide a diverse choice of items and services at their fingertips. The ease of comparing pricing and reading reviews online also improves the buying experience, allowing students to make more educated choices.

CONCLUSION

Finally, the study provides valuable insights on UniSZA undergraduates’ internet shopping behavior. It emphasizes the importance of factors such as attitude, trustworthiness, loyalty, security, and convenience in developing students’ online purchase choices. Students are very engaged in the online purchasing process, actively looking for the greatest discounts, exploring new platforms, and enjoying a personalized shopping experience. Trust and security are critical in maintaining students’ confidence, while convenience is the most important reason for them to favor online shopping than traditional retail. Understanding the interplay of these elements allows retailers online to better respond to the needs and interests of student customers, resulting in a more specialized and competitive shopping experience.

ACKNOWLEDGMENTS

The authors would like to thank all respondents from various Faculties of UniSZA for their participation in data collecting and voluntary participation in this research.

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