Factors affecting Malaysian Consumers’ Online Shopping Behaviour

Abstract

The purpose of the study was to examine the factors affecting the online shopping behaviours of consumers in Malaysia. The research employed a self-administered survey to collect empirical data from 150 online shoppers in Malaysia by using convenience sampling. Cronbach alpha was calculated to confirm the reliability of the data and then normality was assessed. IBM SPSS was utilized for data analysis. The demographic of the respondents revealed that young people are the most online shoppers while their educational qualification too also plays a role. The findings from the research indicate that all factors that are considered as explanatory variables for this work have positive impact on online shopping behaviour of the consumers. The findings of the study offer some emerging perspectives that could be leveraged on in order to improve online shopping behaviours of the consumers in Malaysia. This study if properly implemented will be of immense help in making online shopping experience a worthwhile for Malaysians. The study further pointed out some limitations encountered for further studies.

1 chapter 1: introduction

1.0 Introduction

This chapter discussed the factors affecting online shopping behaviour of consumers as online shopping has become a modern shopping form. It is now adopted around the world. This shopping method is still not as known or embraced in many other countries and while online shopping awareness is now starting to develop rapidly.

1.1 Background of the study

The type of shopping for goods or services over the internet is Internet shopping (also known as online shopping and Internet shopping and purchasing). The process consists of five steps similar to traditional buying practices (Li and Zang, 2002, p. 508).

Online Shopping is a type of electronics trade that enables customers, through the web browser, to directly buy goods or services from sellers. Internet shopping is a phenomenon that develops exponentially (Ganapathi, 2015, p. 123). Increasing numbers of customers are shopping online for products and services, collecting product information or even searching for fun. Online shopping catches up to desktop shopping easily; it’s easier to do than people always go. Mobile shopping allows you to shop from anywhere when the service is open. That is why 49% of people use their telephones solely for shopping. This means 49% of your traffic is also supplied by smartphone users. Because of this it is important to optimize your mobile site. The rise in mobile shopping and physical shopping is exceptional. Mobile is projected to dominate online sales by 2021, which accounts for 54 percent of overall sales.

Figure 1.1: Statistical analysis of online shopping from 2017-2021

As the market prospects are increasing exponentially, a variety of services are provided on the internet. Internet shopping has been one of the most popular internet-based services. It has huge benefits for both customers and companies. Business have been able to attract more customers at a reduced cost by on-line shopping. They were able to meet customers in remote areas. In reality, they serve as steps towards the global village idea. Other than inventory management fees, online shopping are also greatly reduced (Ganapathi, 2015, p. 123).

Consumers can shop from any location and do not need to visit the stores for shopping purposes physically. So even if the customer remains in the remote area, they can shop easily through the internet. Consumers will however, visit many places here to reach the final option. Online shopping therefore provides customers with infinite options in the nut shell. The customer will shop at any time of day of the year. It also aims to save time and resources for customers. Moreover, because of infinite options and less time, customers are able to quickly search for the desired items and compare the products (Ganapathi, 2015, p. 123).

The internet has grown rapidly in the last two decades and the related digital economy is also evolving globally, powered by information technology. Following the long-term growth of the Internet, which rapidly improves website users and high-speed Internet connections, and the development and use of digital technologies for website development, businesses will promote and expand product and services images by means of websites. Thus, comprehensive product details and enhanced service draw more and more people from conventional fashion to rely on Internet shopping to shift their consumer behaviour (Ujwala and Vinay, 2012, p. 1). In the other hand, more businesses have recognized that the evolution of customer behavior is imminent and hence their marketing strategy has shifted. As research has recently shown, internet shopping has increased and online shopping has become more popular for manypeople particularly in business to consumers (Ujwala and Vinay, 2012, p. 1).

Traditional product shopping has changed and grown entirely. The Internet has created a forum for customers to shop intelligently. Consumers can explore different areas free of charge broad variety of alternatives and select the best. Many businesses use the Internet for the purpose of lowering costs and thereby reducing product prices. It also helps them reach a broader audience that wants their product (Devkishin et al., 2013, p. 1). Customers are currently using the internet not only to buy the product but also to compare goods and costs and find out the advantages of shopping the product (Devkishin et al., 2013, p. 1).

For several purposes and reasons in the business cycle of consumer e-commerce consumers use the Site for: searching for product functionality, pricing or reviews, availability of goods and services over the Internet, ordering, payment, or any other means accompanied by online or other supply of products. Online consumer study has been conducted in many fields, including information technology, brand strategy, psychology and social psychology. Online Shopping activity (also known as online shopping and internet shopping/commerce behaviour) refers to the way items or services are purchased over the Internet.

1.2 Problem statement

Online shopping is increasing so rapidly that the global size of the online shopping industry is expected to exceed 4 trillion in 2020. 300 million online shoppers are foreseeing in 2023 alone in the US. So far, 69% of Americans have been shopping online and 25% at least once a month have shopped online. Most (59%) bought clothing and 47% bought their first item on Amazon. However, not Americans are the only people who shop online. The benefits are enjoyed by the people around the world. Accorded by Invesp, the most popular countries for shoppers are USA (1804 dollars), UK (1 629 dollars), Sweden (1446 dollars), France (1228 dollars), Germany (1064 dollars), Japan (968 dollars), Spain (849 dollars), China (626 dollars) (396 dollars), Russia and Brazil (350 dollars).

Figure 1.1 Average Revenue Per online Shopper

As of 2018, the worldwide penetration rate for digital buyers is 47.3 percent. If we look at gender figures, the gap between the share of men and women is not a stopper – 72% are women, while 68% are men.

The biggest difference between men and women is what they do. Men typically go to heavy goods such as furniture and computers, while women mostly go to clothing and food.

Social media has become an increasingly important platform for brands and customer interaction in recent years. Current online marketing literature is nearly new and illustrates, for the most part, the advantages of online engagement between the company and the user (Eugenia, 2015, p. 1). In online innovative design, promotion allows customers personalized design services, but it does not mean that customers automatically work again with designers, even though they are happy with design and not with customer service. Some design companies that support online focus only on graphics solutions rather than on using them to control the actions of customers that will hopefully return with not only good design service but also great customer service; the same is true for innovative design companies. Those that use social media to influence the purchasing of customers do not primarily focus on customer service.

The internet has become a very popular medium of advertising, not just because the internet is becoming omnipresent, but also because it provides some advantages over other types of advertising Eugenia (2015, p. 1). This feature helps the user to communicate and learn more about the product/services with the advertiser. Other internet benefits such as versatility, 24-hour access and global reach make the internet a preferred platform for marketers to view and market their items.

Various styles of shopping can be observed on internet platforms. Over the years a number of studies have been carried out to explain the impact of online shopping on respondents’ perceptions and purchasing behaviour. These researches tried to use different models to illustrate the efficacy of online shopping. For marketers, evaluating the effectiveness of online shopping is critical. Different approaches have been developed to control the performance of these shopping. The methods used to calculate the efficacy of such ads, however do not vary substantially from those used in traditional media. This is because the reaction of consumers to online shopping is close to traditional media according to (Eugenia, 2015, p. 2). For example, measurement attitudes used to assess online shopping efficiency and effect are comparable with those used in traditional media: liking, advertising behavior and brand attitude (Eugenia, 2015, p. 2). Given the enormous amount of money spent on online shopping, it is necessary for businesses to determine its effect on the purchasing habits of the users. It is important to know which problems influence the attitude of web users towards online shoppers. However, online shopping cannot be effective without online advertisement (Eugenia, 2015, p.3).

Researchers on the model theory adopted have been able to use it in fields such as online shopping, resulting in the creation of other models, including the online shopping acceptance model. Theoretical and practical research is therefore needed to fill the gaps in online shopping. Hence the theoretical model used in the current study to interpret the data was based on the model of technology acceptability in order to enhance or substitute goods and services. This acceptance model explores the perceptions and experiences of consumers.

1.3 Research question

There are several research questions proposed as follows.

What is the influence of price factors on the online shopping behaviours of the consumers?
What is the influence of perceived risk on the online shopping behaviours of the consumers?
What is the influence of perceived usefulness on the online shopping behaviours of the consumers?

1.4 Research objective

The general research objective of this study is to examine into the determinants that impact on the online shopping behaviours of consumers. There are 3 specific research objectives to be listed as follows.

To examine into the influence of price factors on the online shopping behaviours of the consumers.
To examine into the influence of perceived risk on the online shopping behaviours of the consumers.
To examine into the influence of perceived usefulness on the online shopping behaviours of the consumers.

1.5 Significance of the study

Based on the many advantages of shopping online rather than traditional, more people today claim that they prefer to buy online. The decision-making process of the consumer in recent years has changed significantly. Buyers perform a comprehensive on-line analysis before they ever speak to a seller. Places of conventional brick and morter are not commonly viewed on their mobile devices by consumers now working mostly online. The Internet is much simpler and quicker to do business. The way people do business with a growing global shopping trend has changed.

The significance of this research work is to assess the impact on the online shopping behaviours of consumers. If online shopping and its customers’ behaviour are established, online sellers will enhance internet activities to attract potential clients. The mediation customer support would enhance the customer service package on the basis of results. Companies and other clients would like to work with and be loyal to the design companies by repeating their order. Consumer service and customer buying can be used interchangeably for this study.

The analysis will include additional information and data on and effect on online shopping. The knowledge collected would thus provide a better understanding of the efficiency and significance of the online advertisement phenomenon. This will help to devise and enforce suitable management and marketing strategies that strengthen the design and effectiveness of online shopping. This study also allows online markets or online sellers to look at ways that make online shopping more attractive and friendly to their various customers to limit customer dissatisfaction. The results of the study would therefore add to the information stock and expand the limits of knowledge in online shopping system in the globe.

1.6 Research scope

The study explores consumers’ perception and attitude towards online shopping and how such expectations influence their purchasing behavior. The research focuses geographically on online shoppers in Malaysia. In Malaysia, the study focuses primarily on people living in Selangor, as the researcher currently studies at KDU University College in Selangor. The study scope is focused on online shopping and customers’ behaviours. The variables used in the study are promotion on Online shopping, customer buying and patronage and customer support, which can all be covered within the broad fields of marketing, consumer behaviour and customer care.

1.7 Definition of key terms

Table 1.1 Definition of Key Terms

Variables	Definition	Sources
Online shopping behaviours	Online shopping refers to the method of shopping for goods or services over the Internet.	Li and Zang, 2002, p. 508
Price	The market price is the current costs for the purchase or selling of an asset or service.	Teodorescu et al., 2008, p. 23
Perceived Risk	Consumer perception of risk was described as an antecedent leading to consumer reluctance in adopting online payments.	Maziriri and Chuchu, 2017, p. 257
Perceived Ease-of-use	Perceived ease of use is the understanding of the consumers that online users are easy to communicate with.	Cheema et al., 2007, p. 133

1.8 Chapter Layout

The thesis is divided into five chapters. Chapter 1 presents the study which addresses the context of the study, the statement of problems, objectives of the study and questions of research, the meaning of the study and the organization. Chapter two reviews the theoretical analysis pertaining to online shopping, pricing and the study perceived. Chapter three deals with the methodology of studies that includes the sampling process, the study techniques and design, the different sources and methods of data collection and the method used for data analysis. Chapter Four offers a comprehensive presentation, analysis, interpretation and discussion of the results of the survey. The conclusion and recommendation of the results in Chapter Five is devoted.

2 CHAPTER 2: LITERATURE REVIEW

2.0 Introduction

Chapter two addressed re-shopping online is one of the growing e-commerce fields. More and more consumers use the World Wide Web to meet their shopping needs, which enable them to access local or international goods with a mouse click. The theme of shopping everywhere draws customers who can’t take time off their busy times to go shopping. With that in mind, a range of online stores have appeared in the Internet selling items from books, music CDs, household goods, food to furniture and automobiles. Consumers may also use a number of contacts, consulting and services.

2.1 Theory proposed

2.1.1 Theory of Acceptance Model

The acceptance theory or acceptance technology model (TAM) as is generally referred to has been adapted to the theory of reasoned action and planned actions. TAM specifically proposes to explain the determinants of the behavior of end-users of IT against IT. In TAM, the influence of external variables on the purpose is mediated via perceptual usability (PEU) and perceived usefulness (PU). TAM also indicates that the objective is directly linked to the actual conduct of use. The tam model is endorsed by many studies (Surendran, 2012, p. 175).

Researchers have developed and used numerous models to understand IT users’ acceptability to recognize, predict and explain why people accept and reject IT systems. The TAM introduced by Al-Smadi (2012, p. 296) is one of the most popular models used by researchers to study underlying factors that enable users to pick up a new information system and integrate it (Al-Smadi, 2012, p. 296). TAM’s primary objective is to explain factors that typically affect the acceptance of computer applications. This model also helps researchers and clinicians to determine if a system is unsuitable (Al-Smadi, 2012, p. 296). Al-Smadi (2012, p. 296) claimed that the use of the information system is directly affected by the behavioral intention to use it, which is determined by the users’ attitude and usefulness to the system. The perceived ease of use also affects the attitude and perceived utility. According to TAM, increased utility and ease of use of an information system would have a beneficial impact on the system attitude. The attitude in turn contributes to a more intense use of the system that has a positive effect on the actual use of the system. TAM assumes that anything else is equivalent, as a technology is more efficient, which is measured by its perceived ease of use.

This model assumes that the application of the system is determined directly by the behavioral goals affected by system perceptions and perceived system utility. The perceived ease of use also affects conduct and perceived utility. The degree to which a person believes his work performance is increased using a system is considered to be useful (PU). The perceived ease of use (PEOU) implies that the device is freely used by a person (Al-Smadi, 2012, p. 296). Perceived utility affects the object of use directly, while perceived ease of use indirectly affects the intent of behaviour. The TAM has not only been assessed as a powerful and parsimonious model that represents the determinant of use of system systems, but also as a valuable tool for system planning, since the system designers can monitor their ease and usefulness in some areas (Al-Smadi, 2012, p. 296). Attitude describes an optimistic or negative assessment of the behaviour.

2.2 Dependent variable

2.2.1 Online shopping behaviours of consumers

The consumer’s purchasing or buying plans are normally an aspect of human behaviour. Consumer shopping behavior is commonly characterized as a series of interventions designed to meet the need to satisfy individuals with different characteristics in their consumption needs. Theories of consumption such as the economist’s approach, psychodynamic approach, rising income theory, behavior and cognitive approach are all based on many different forms of theory (Weekeza and Sibanda, 2019).

According to Zhou et al. (2007), online shopping is becoming increasingly popular since the late 1990s as there is a boost in the number of consumers that purchasing various products through the internet. Online shopping behaviour also refers to consumers psychological state in terms of making purchases on the Internet (Li and Zhang, 2002). A common perception that shopping online is generally a fast and convenient way to purchase goods or services than the traditional shopping. Online shopping is definitely benefitting a lot of people by helping them to save crucial time. It is a common phenomenon and a new type of retail shopping that is breeding passionately and now been adopted all over the world nowadays with relevant digital economy that is driven by information technology.

In this modern era with the well development of fast speeding internet connection, new technology has been developed for web developing purposes and therefore leads to an introduction of detail product information and improved service available online that attracts more people switching their consumer behaviour from the traditional way to online shopping (Kumar & Dange, 2012). Online shopping is a process without involving any intermediary or third party which the consumers can purchase goods and services directly through the internet 24/7 with the condition of having internet access (Sunitha & Edwin, 2014).

Liang and Lai (2000) mentioned that the process purchasing products and services through the internet involving five steps similar to traditional shopping behaviour. The only different is that there is no need to visit the official visual store to shop and make comparison on the pricing of similar products across different stores which can be done easily through online. This indicates that consumers that practices online shopping are less technically at risk compared to those who shop directly (Shranck et al. 2006).

In the research done by Li et al, (1999), a linear regression model is used to test the factors that could be used to predicts a customer’s online buying behaviour. There are few exogenous factors that are considered by other researchers that will affecting consumer shopping behaviour.

Simple to use, useful and enjoyable online shopping, and exogenous factors also include customer trait, circumstance, product characteristics, previous experience and confidence in online shopping online Online shopping (Ruyter, 2004). According to Sirrka and Peter (1996), the factors which affect electronic shopping are product perception, shopping experience, customer service and perceived consumer risks.

The study done by Dai et al. (2014) shows that online shopping experience does not only influences consumers’ trust perceived risk. Juniwati (2014) also mentioned that perceived risk and attitude is significantly influence the intention of online shopping but the perceived usefulness, ease of have not significant influence toward intention to shop online. However, Andrew (2012) has a different perspective which is shown in his research that there is an inverse relationship between perceived risk online with consumers’ attitude however that attitude has a positive influence on intentions to continue purchasing.

2.3 Independent Variables

2.3.1 Price

The current market price is the cost for buying or selling an asset or service. The market price depends on the capacity to deliver and demand for an asset or service. The price the quantity is equal to the amount requested is the price on the market. For the estimation of consumption and financial surplus the market price is used. Consumer surplus is the disparity between the consumer’s ability to pay the commodity and the price of the good or the price of the business. The economic surplus is attributed to two amounts: the surplus of the user and the excess of the producer. The excesses of producers are also known as profit: it is the value of producers when sold at market rates (provided that the market price is higher than the least that they would be willing to sell for). The combined excess demand and the excess producer are the economic excesses (Teodorescu et al., 2008, p. 23).

2.3.1.1 Marketing Price

The market price of products or services will adjust with product or service supply or demand shocks. An inattended incident that unpredictably alters the supply of goods or services is a supply shock. A shock to demand is a sudden phenomenon that increases or decreases product or service demand. Examples of supply shock include declines in interest rates, tax increases and state subsidies, terrorist attacks, natural disasters and financial markets crashes. For example, shocks to demand include heavy rises in oil and gas or other resources, political insecurity, natural disasters and production technology breakthroughs.

The most recent price for the sale of a security is the stock market price. The market price is the product of the interaction of traders, investors and bursaries. To make a deal happen at the same price, a buyer and a seller must meet. Buyers represent deals and sellers represent offers. The bid is the highest price, while the price that someone offers is the lowest price. For one stock that could amount to $50.51 and $50.52. If purchasers do not accept that the price is fair, the deal can be reduced to $50.25. The vendors may or may not agree. Someone can lower their bid or remain where they are. Trade takes place only if a seller interacts with the sale price or a buyer interacts with the price of the offer. Offers and deals are continuously changing as buyers and sellers adjust their viewpoints on the price they buy or sell. When selling deals, prices often decrease or buyers increase prices from the auction.

2.3.1.2 Determination of price

The above-mentioned knowledge of the profitability threshold is not adequate to properly handle price policy. An efficient price determination is also important in order to achieve a profit that does not only cover costs and their recovery in this market economy. Differentiate in the prices expected between basic (or catalog) prices and final and resulting prices should be calculated by a pricing strategy that involves mixed sales and discounts, the correspondence between the prices of the product range (Teodorescu et al., 2008, p. 23). Furthermore, some of the main elements and aspects to take into account in deciding basic market prices will be mentioned:

incorporation of benefit in the price equation;
recognizing future consumers;
Competition response anticipation;
Industry quota establishment;
Selection of pricing strategy for the target category of the market;
The link between commodity policy and channel distribution and promotion and pricing strategy

2.3.1.3 Price and Customers

Price is closely related to the customer as a marketing tool. The producers use different calculations and assumptions based on costs and benefit to predict the various prices. You must always keep a close eye on competition and customers. The biggest challenge for price projectors in their consumer relationships is the profit margin. If this does not align with the actual possibilities of the market that the profit margin is too high relative to the options of a purchaser, the risk is that the product is not met, that no profit is reached or that no cost will recover necessary to market the product or the service. This is why it is worth considering the thorough study of the buyer’s reaction, rationing and attitudes with regard to the price of the merchandise to be purchased (Teodorescu et al., 2008, p. 28).

The general research schemes of purchaser reaction to the price are based on the general theory and the global model known in the literature as the Marshall model for the study of customer behaviour. This model ties the customer’s price perception to their sales and also proposes to take economic factors into account in assessing behavioral reactions. The same author maintains that consumers use the “money counting rule” to calculate the level of impact on their satisfaction of the goods (Teodorescu et al., 2008, p. 29).

Consumers are thus particularly sensitive to price levels, and producers tend to be well aware of this sensitivity. The buyer’s aversion to price is usually demonstrated by the product or service curve in market research. The price sensitivity curves are extremely useful and are commonly used in the determination of market price and strategy (Teodorescu et al., 2008, p. 29). The following schemas provide an eloquent explanation of calculating price sensitivity.

Figure 2.1 Calculation of price sensitivity

For a given commodity, price sensitivity curves are made and relate to a well-established time span. At the same time, the exchange unit (retail, kg, liter and package) must be taken into account in order to make the price.

H1: Price factors can significantly influence the online shopping behaviours of the consumers.

2.3.2 Perceived Risk

Based on uncertainties regarding the handling of transactions, a consumer perception of risk was described as an antecedent leading to consumer reluctance in adopting online payments (Maziriri and Chuchu, 2017, p. 257). Maziriri and Chuchu (2017, p. 257) have indicated that perceived danger drives customers to seek additional details. Many scholars have discussed threats in different contexts in previous literature similar to the present research. Maziriri and Chuchu, 2017, p. 257) investigated the risk perceived by Maziriri and Chuchu (2017, p. 257) examined post-purchase risk. Maziriri and Chuchu (2017, p. 257) postulated social risk as a mediator in the relationship between situation participation and perceptions of confidence.

According to Maziriri and Chuchu (2017, p. 258), Bauer originally developed the definition of perceived risk in 1960. He pointed out that purchasing behaviors from consumers could lead to difficult and even undesirable results. Consequently, the customer decision involves the ambiguity about the outcome that was the original definition of perceived risk (Maziriri and Chuchu, 2017, p. 258).

Maziriri and Chuchu (2017, p. 259) view the risk as a result of purchase decisions or the potential effects of incorrect choices. The perceived risk is a design that measures convictions of uncertainty as defined by Maziriri and Chuchu about possible negative results. A perceived risk was formally described in the field of consumer behavior as a combination of uncertainty plus the magnitude of its effects and the likelihood of loss associated with purchases (Maziriri and Chuchu, 2017, p. 259). The perceived risk refers to the essence and degree of the customer when deciding on a certain purchase (Maziriri and Chuchu, 2017, p. 259). The most common concept of perceived risk is the subjective consumer’s perception of failure that means that any consumer behavior will yield outcomes that are not predictable and sometimes negative.

Perceived risk greatly guides consumer behaviour, as people want to prevent errors (Maziriri and Chuchu, 2017, p. 259). As only in future will the consequence of a decision be understood, the consumer must face uncertainty and in the sense that consumers understand they cannot meet all their purchase goals, the danger is perceived Maziriri and Chuchu, 2017, p. 259). Perceived risk is a consumer’s uncertainty when it comes to purchasing products, particularly expensive items, such as vehicles, houses and computers. Whenever a consumer considers the purchase of a product, the consumer has some doubts, particularly when the product is highly priced.

The descriptions of the various forms of risk are very heterogeneous in the research papers studied. In the literature perceived risks associated with money loss were defined as monetary risk or financial risk or economic risk. The monetary risk also included future losses arising from unforeseeable costs applied to the original product price. In some research, this risk category includes losses related to fraud, including credit card misuse, leakage of personal information and non-received goods. The literature defines risks related to the expected product performance as a product risk, quality risk, or performance risk or functional risk. The perceived time risk includes all forms of time-consuming losses, such as loss of time as a result of information and transaction searches as well as product distribution, replacement or repair. Risks related to data protection involve damages arising from the assumption that unauthorized individuals are able to use their personal data without user consent.

The perceived risks of delivery are related to the loss of inadequate delivery (wrong delivery place, damaged goods, long delivery time, etc.). Some literature definitions also include packaging and transportation handling Balogh and Mészáros (2020, p. 17). Perceived after-sales risks are related to the possible damages incurred by the difficulty of contacting the seller and enforcing customer rights. The perceived risk definition has been a highly explored and successively expanded field over the last 60 years. Balogh and Mészáros (2020, p. 15) reported a two-component model with the following dimensions: complexity and dangerous implications.

Balogh and Mészáros (2020, p. 15) found that consumers preferentially favor different risk reduction strategies associated with different forms of failure. Balogh and Mészáros (2020, p. 16) recorded the five perceived risk types: efficiency, physical, emotional, social and financial performance. Roselius introduced the time dimension to the risk form definition at the same time. With the growth in product variety and contact noise around them the consumer has found it difficult to be completely informed about product offers Balogh and Mészáros (2020, p. 16) defines six categories of risk: mechanical physical, economic, social, psychological and time hazards.

Consumer online shopping activity began to be studied at the beginning of the 1990s. A growing number of longitudinal studies have explored the effect of perceived risk on buying attitudes. In connection with this the work of Balogh and Mészáros (2020, p. 16) must be stated. Their research identified four perceived risk types that were important to shoppers online: financial product efficiency, psychological, and the risk of time/comfortability/loss.

2.3.3 Perceived Usefulness

Customers are of the view that their efficiency would be improved after online shopping. It should be useful and have an impact on online shopping. The perceived utility becomes the determiners of users’ scheme, acceptance and behaviour. A technology may be said to be effective if it has the customer’s usefulness. It would be used by device users where the system is useful; whether the system is easy to use or not (Iriani and Andjarwati, 2020, p. 314). Iriani and Andjarwati (2020, p. 314) indicated how beneficial it is to assume that using a specific system would boost its performance. The study of Iriani and Andjarwati (2020, p. 315) supports the idea that perceived utility will affect the online shopping decisions of consumers. Iriani and Andjarwati (2020, p. 315) also suggested that perceived utility has a positive impact on on online shopping attitudes and intent. The research from Iriani and Andjarwati (2020, p. 315) indicates that the perceived utility variable partly affects the decision of online shopping. This outcome is reinforced by Iriani and Andjarwati (2020, p. 315) which show that perceived utility has an important and positive impact on online shopping decisions. In order to calculate the perceived usefulness variable in this analysis, the author uses the following indicators: work faster, job performance, improve productivity, reliability, promote work and make work useful.

Perceived usefulness is characterized as the degree to which a person believes a particular system will increase his or her performance at work. Ultimately, internet purchases are abused because they find systems useful for their financial transactions. In the Information Systems (IS) community comprehensive research has been conducted to show the important impact of perceived utility on usage intent (Mat and Sentosa, 2008, p. 468). Mat and Sentosa (2008, p. 468) has determined that perceived utility has an important influence on the purpose to do Internet banking while Mat and Sentosa (2008, p. 468) has found a stronger effect on use. The study by Davis indicates that the use of technology is motivated primarily by its functions and secondly by its ease of using these functions. If the service provides desperately needed functions, consumers are often prepared to ignore such problems of use. In the previous George study, perceived behavioral control was not significantly related Mat and Sentosa (2008, p. 468).

Perceived usefulness is a person’s belief that using a new method helps him/her make changes in their job output. Perceived ease of use is the impression of a person that the introduction of a new system or technology needs no expense or effort. The human experience is viewed as a pleasure by introducing new systems or technologies. In addition, if a person sees that utility facilities outweigh the effort needed to use the Internet, he/she will use the internet to make online shopping. Two important factors that influence online shopping intentions are the useful (extrinsic) and hedonic (intrinsic). The useful value is financial gain and cost estimation, while the hedonic value is an appraisal of experiential costs and benefits (Cheema et al., 2007, p. 132).

In terms of e-shopping, consumers have a belief that their efficiency is increased by shopping online. The presumed utility is the utilitarian element that affects online shopping (Cheema et al., 2007, p. 132). TAM (Cheema et al., 2007, p. 133) reports that consumers are likely to use the online website that has an important effect on their success. According to Cheema et al. (2007, p. 133) the consumer is likely to improve its productivity by shopping online and that affects the whole buying process positively. Cheema et al. (2007, p. 133) notes that consumers tend to buy a commodity if such use is deemed useful.

H3: Perceived usefulness can significantly influence the online shopping behaviours of the consumers.

2.4 Conceptual Framework

2.5 Summary

This chapter discussed the main three factors affecting online shopping and how it may affect the customers’ behaviour. The technology acceptable model was also discussed in the chapter. The importance of the various factors was analysed and examined carefully in the chapter.

3 chapter 3: methodology

3.0 Introduction

In Chapter 3, research methodology will be explained and determined the connection among the variables before reaching to the implementation phase of the subsequent chapter. Research methodology consists of several subchapters which includes study design, data collection procedure, research area, study population, sampling design, instrumentation, measurement of constructs, ethical consideration on respondents and method of hypothesis testing.

3.0 Study design

According to Akhtar (2016), study design which is also known as research design which is the structure of the research that combines all the elements together as a plan of the proposed research work. Research design also provides an overall structure for data collection method (Leedy, 1997).

In this research, quantitative descriptive method is used as the empirical assessments to examine the relationship between the three independent variables which are price quotations, visual satisfaction and payment options and security towards consumers’ online shopping behaviour (dependent variable). Quantitative research is a method commonly used in conducting a research as the researcher will explain an issue or phenomenon by collecting the data in numerical form before analyzing with the aid of mathematical methods or in other word statistics (Aliaga & Gunderson, 2002). Quantitative method is a better option as compared to qualitative method for a large targeted populations sample size which involves several types of measurement and analysis (Sekaran and Bougie, 2012).

3.1 Data collection procedure

According to Neuman (2016), data collection is a systematic method of gathering data for a specific purpose either in qualitative or quantitative approach. Data collection is conducted over primary data method in this research. Primary data is used where the data was collected in the form of questionnaire. There will be no previous records of the data will be accessed by the public as the questionnaire is conducted with a specific purpose.

In the beginning of February 2020, the spread of the Corona virus outbreak that occurred has impact the people from all around the world not only on the aspect of economic but social and psychological as well (Iriani & Andjarwati, 2020). Until now, 218 countries and territories around the world have been confirmed to be affected by the Corona Virus which originated from Wuhan, China and which Malaysia is one of them with a total of 68,020 reported cases and 365 death cases as shown in the figure below (Worldmeter, 2020). This is believed to make Malaysian began to feel fear, suspicion and anxiety as the number of death cases increasing gradually from time to time. In order to limit the spread of the Corona virus outbreak, Malaysia government enforced the Movement Control Order starting from time to time with several regulations have been practices by Malaysian such as social and physical distancing, large-scale social restrictions and regional quarantines (Md Shah et al, 2020). Online questionnaire is selected as the instrument used in this study not only due to the challenges and circumstances brought by the COVID-19 crisis. But it is also a rapid and cheap data collection method which is efficient in the midst of crisis. Online questionnaire is also environmentally friendly way as compared to the traditional method which is needed for the sake of expediency.

Figure 3.1 Total Coronavirus Cases in Malaysia

Source: Worldmeter, 2020

Figure 3.2 Total Coronavirus Deaths in Malaysia

Source: Worldmeter, 2020

3.2 Research area

The field of study will be the field of research. In malaysia, a nation of Southeast Asia, lying north of the Equator, the field of research is situated. Two regions, Malaysia Peninsular or Western Malaysia and East Malaysia, constitute Malaysia. The research is carried out predominantly in Malaysia, with the residents of Selangor studying at KDU University College in Selangor. Furthermore, according to the population distribution obtained by the Statistics Department of Malaysia (2020) by state statistics it was demonstrated that Selangor, Malaysia is the country with the largest population, as shown below, totaled 6.54 million people.

Figure 3.3 Demographic Statistics by State, Third Quarter 2020, Malaysia

Source: Department of Statistics Malaysia (2020)

3.3 Study population

According to the Digital 2019 Malaysia report which is supervised by Hootsuite and Wearesocial, it shows that 26 million out of the country’s population of 32 million are active internet users. Out of the 26 million, around 80% of them falls in the age category between 16 and 64 are experiencing online shopping with 91% of them are active users. Therefore, the target population in this study is Malaysia residents whoever aged 16 years old and above. Younger population (16 to 18 years old) which is also known as Digital Native which is not financial independent is targeted in this study. The major reason to include them in the research is they are comfortable with technology and computers which can process website information five times faster than the older generations. This is also relating to their growing environment as they grew up in the information age and prone to use the media on daily basis (Mazzini, et al., 2016).

3.4 Sampling design

3.4.1 Sampling Frame and Sampling Location

The sampling was described as a “process to select the right persons, objects, or events to represent the whole population by Sekaran and Bougie (2012). The frame of sampling is defined as a list or database of potentially selected participants (Stephanie, 2014). This study does not have a sufficient sampling structure as any customer with or interested in Malaysian online shopping may be involved in this analysis.

Malaysian social media users invest on the sites on average five hours 47 minutes daily, as illustrated in the figure, following recent research by YouGov. This shows Malaysians are on social media a quarter of an hour a day. In other words, social media plays an important role to connect people, create communities, and share your views and marketing and advertisement for companies. The Malaysian Communications and Multimedia Commission study from the Internet users’ survey 2018 estimated that there were around 24.6 million users of social networking, of which 97.3% owned a Facebook account (Suruhanjaya Komunikasi dan Multimedia Malaysia, 2018). As the most preferred social networking site in Malaysia, the researchers will distribute the questionnaire via Google Facebook Applications.

Figure 3.4 Malaysian social media usage and habit

Source: Kim, 2019

3.4.2 Sampling Size

According to Sekaran (2012), sampling size refers to a group of individuals selected from the whole population to represent as a subset of population. In this research, G-Power is used to determine the sampling size in this research. Assuming that effect size is 0.09 with 95% degree of freedom, 3 number of predictors are tested using liner multiple regression model, a total of 122 questionnaire must be collected in this research (Appendix 3.6).

3.5 Instrumentation

3.5.1 Questionnaire Design

Pre-designed questionnaire is used to examine the factors that affecting an online shopping behaviour of consumers. According to Bradburn and Sudman (2011), the questions should design in a way focusing on the current respondent attitude and behavour for the accuracy of the research. A well- structured questionnaire plays an important role in the research to ensure a smooth and easy data collecting process.

Structured and closed-ended questionnaire is used in this study. The questionnaire is designed in English which consists of three sections. Section A will gather respondents’ demographic profile which is in nominal scale, ordinal scale and ratio scale. Section B is concerned with the type of questions which is related to the consumer’s online shopping behaviour in Malaysia. While Section C is intended to examine the factors which includes price quotations, payment options and security and visual satisfaction affects an online shopping behaviour of the consumers. Both Section B and C are measured using 5-point likert scale method.

3.5.2 Pilot Test

Pilot test is a process undergoes before distributing the actual questionnaire is carried out for large-scale audiences to ensure that the information collected is true and worthy (Bird & DomineyHowes, 2008). A pilot test is important for refining questionnaire, identifying errors and enhancing the questionnaire (Zikmund et al., 2003). According to Issac and Michael (1995), the most adequate number of pilot test to be distributed is between ten to thirty respondents. Therefore, a total of 30 sets of questionnaires will be distributed online to run the pilot test. A reliability test using SPSS is carried out after collecting the data to ensure the accuracy and reliability of the data collected. The respondents who involved in the pilot test will be excluded in the actual survey later on.

3.6 Measurement of constructs

3.6.1 Dependent variable

Table 3.1 DV Questionnaire

Variable	Questionnaire Elements	References
Online Shopping Behaviour of Consumers	1) I intend to use online shopping within the near future	Maiyaki, 2016
	2) I should consider using online shopping rather than traditional shopping	Maiyaki, 2016
	3) I will definitely shop online in the future	Thamizhvanan & Xavier, 2012
	4) I already shop online	Gong, Stump, & Maddox, 2013
	5) Shopping online is a very good idea	Gong, Stump, & Maddox, 2013

3.6.2 Independent variable

Table 3.2 IV Questionnaire

Varriable	Questionnaire Elements	References
Price	I feel that online shopping would save money	(Maiyaki, 2016)
	I feel that the price offered at online store is consistent	(Maiyaki, 2016)
	I think the product’s price labelling in online shopping is clear	(Maiyaki, 2016)
	In online shopping, a higher price of a product will result in better quality of the product.	(Maiyaki, 2016)
Perceived Risk	I might not get what I ordered through online shopping	(Vaghela, 2017)
	It is hard to judge the quality of merchandise online	(Vaghela, 2017)
	I feel that my credit card details may be compromised and misused if I shop online	(Vaghela, 2017)
	I might not receive the product ordered online.	(Vaghela, 2017)
Perceived Usefulness	Using the Internet enables me to finish my shopping tasks more quickly	(Vaghela, 2017)
	Using the Internet for shopping helps me to make better purchase decisions	(Vaghela, 2017)
	Using the Internet makes it easier to make purchases	(Vaghela, 2017)
	Overall, I find using the Internet for shopping useful	(Vaghela, 2017)

3.7 Ethical consideration on respondents

Ethics is described as “a correct code of behavior” (Pera and Van Tonder,1996). The ethics of the research groups are mindful of their roles and duty, including informed consent, right to secrecy and confidentiality, the right to privacy, justice, charity and respect for individuals (Brink & Wood, 1998).

In order to guarantee qualitative analysis the scientists should pay more attention to certain factors in order that ethical codes are not broken. Confidentiality and privacy are one of the main ethical concerns. In compliance with the Personal Data Protection Act 2010 (PDPA), which came into force on 15 November 2013, all data collected in this research are kept confidential and privately owned, a contact on a questionnaire cover-page is attached and a consent to collect, register, store, use and store personal information is informed.

3.8 Method of hypothesis testing

The testing of hypotheses is a method for testing the connections between the dependent variable and an independent study variable. It also serves as a reference for the theoretical structure system. This study explores the association relationship between the variables as well as the important relation between the independent and dependent variables.

3.8.1 Multiple Linear Regression Analysis

The statistical approach for the evaluation of the relationship between one dependent, Y and two or more different, variables is a multiple linear regression analysis (X1, X2,…, Xn). An all-round system for data analysis in behavioral, social, biological and technical sciences is Multiple Regression Analysis (Cohen & Cohen, 1984). Multi-regression analysis is the most effective approach for analyzing the relation between independent variables and the dependent variable (online shopping behaviour of consumers).

In order to calculate the model’s goodness, R-squared and the modified R-squared will be introduced. Modified R2 means the variance of dependent variable, after modification of the degree of freedom, is clarified by all independent variable. Although checking with t-test is the relevant impact of the independent variables on the dependent variables.

For multiple regression analysis, the formula equation is as follows:

3.8.2 Reliability Test

Reliability is an indicator of measurement stability and coherence to access the ‘goodness’ (Zikmund et al., 2010). In order to ensure the reliability of the values for specific and qualitative analysis, the test for reliability is employed to analyze the consistency between independent and dependent variables. The measurement reliability used in this research is the alpha coefficient of Cronbach (Cronbach & Shavelson, 2004). It shows how well the things in a collection are positively connected; the closer the alpha coefficient of Cronbach, the more accurate the interior consistency.

Table 3.3 Rule of Thumb for Internal Reliability Test

Cronbach’s Alpha Coefficient,α	Level of Reliability
α ≥ 0.80	Excellent Good Reliability
0.70 ≤ α ˂ 0.80	Good Reliability
0.60 ≤ α ˂ 0.70	Fair Reliability
α ˂ 0.60	Poor Reliability

Source: Zikmund et al. (2010)

3.9 Summary

In this chapter, it discusses the types of research methodologies used in conducting this research. Quantitative method is used in this study and primary data is chosen as the data collection method which the researcher will distribute the questionnaire online by using Google Form through Facebook apps.

4 chapter 4: data findings and analysis

4.0 Introduction

This section focuses on the econometric analyses and interpretation of results. The chapter starts with the descriptive analysis of the secondary data used for this study. Particularly, the descriptive analysis focused on the summary statistics of the data and this includes the mean, median and some charts that can best describe some variables. Furthermore, the chapter presents some statistical tests that are carried out in order to establish the strength of relationship that exists between variables under consideration for this study.

4.1 Procedure of Data Analysis

The raw data collected was sorted and edited as the first step towards its analysis. The questionnaires were organized and classified according to the patterns given by the respondents and their homogeneity. The responses from the questionnaires were organized in line with the research questions and descriptive narratives were used to reflect the situation as it occurred at these sectors. Both descriptive and inferential statistics were used in the analysis of the data. Inferential statistics included frequencies, regression and some other preliminary tests. The analysed data was summarized and findings were reported in both descriptive and inferential form of statistics study

4.2 Data Screening and Data Cleaning

Data screening and examination was conducted using descriptive and inferential statistics by frequency distribution and independent sample correlation analysis. The sample of the study was carefully selected among the users of online shopping in Malaysia. An assessment of missing data was conducted while a rigorous examination was done in order to detect the outliers in the data. Characteristics of the respondents was carefully re-examined while a test of response bias was also conducted in order to determine the degree of data cleaning. Based on the outcome of these tests, it can be concluded that there is no relevant data that is missing and likewise the data did not contain outliers.

4.3 Analysis of Central Tendency

4.3.1 Online Shopping Behaviours

Table 4.1 Purchase Behaviours

Descriptive Statistics
	N	Minimum	Maximum	Mean	Std. Deviation
DV1	150	1	5	4.17	1.006
DV2	150	2	5	4.27	.946
DV3	150	1	5	4.25	.921
DV4	149	2	5	4.19	.982
Valid N (listwise)	149

A mean response of <1 implies not at all, mean response of value 1.1 to value 2 implies to a less extent, mean response of 2.1 to 3 implies moderate extent and mean response of 3.1 to 4 implies a large extent and 4.1 to 5 implies very large extent.

As regards Purchase Behaviour, table 4.1 above revealed that to a very large extent, the respondents intend to use online shopping within the nearest future (mean of 4.17). Furthermore, the mean result of (4.27) from the respondents indicates that the respondents are considering using online shopping rather than traditional shopping to a very large extent. To a very large extent, the respondents are certain about their future use of online shop (mean of 4.25). Lastly, a mean of 4.19 revealed implies that majority of the respondents are already shopping online to a very large extent.

4.3.2 Perceived Price

Table 4.2 Perceived price

Descriptive Statistics
	N	Minimum	Maximum	Mean	Std. Deviation
PP1	150	1	5	3.85	.932
PP2	150	1	5	3.73	.960
PP3	150	1	5	3.68	.907
PP4	150	1	5	3.61	.947
Valid N (listwise)	150

A mean response of <1 implies not at all, mean response of 1.1- 2 implies to a less extent, mean response of 2.1 – 3 implies moderate extent and mean response of 3.1- 4 implies a large extent and 4.1 to 5 implies very large extent.

As regards Perceived price questionnaire, it was inferred from table 4.2 that the respondents use online shopping to a large extent to because they feel that it would help them save money (mean of 3.85). To a large extent, the respondents indicate that they prefer online shopping because they feel that the price offered at online store is consistent (mean of 3.73). Also, the respondents use online shopping to a large extent because they think that the product’s price labeling in online shopping is clear (mean of 3.68). To a large extent, the respondents indicate that they prefer online shopping because they feel that a higher price of a product will result in better quality of the product (mean of3.61).

4.3.3 Perceived Risk

Table 4.3 Perceived Risk

Descriptive Statistics
	N	Minimum	Maximum	Mean	Std. Deviation
PR1	150	1	5	4.05	1.035
PR2	150	1	5	4.05	.982
PR3	150	1	5	3.90	1.073
PR4	150	1	5	4.06	1.057
Valid N (listwise)	150

As regards Perceived risk questionnaire, it can be inferred from the mean result in the table 4.2 above that the respondents to a large extent bear in mind the risk that is associated with online shopping because they think they might not get the exact product that they ordered for (mean of 4.05). Also, the mean value of 4.05 implies that respondents are sceptical of online shopping to a large extent because it is hard to judge the quality of merchandize online. To a large extent, the respondents indicate that they are conscious of the risk of online shopping because they perceived the risk associated with their credit card due to internet fraud (mean of 3.90). Lastly, the respondents indicated that the risk of not receiving the product ordered online is perceived to a large extent as part of the risk associated with shopping online (mean of 4.06)

4.3.4 Perceived Usefulness

Table 4.4 Perceived Usefulness

Descriptive Statistics
	N	Minimum	Maximum	Mean	Std. Deviation
PU1	150	2	5	4.03	.972
PU2	150	1	5	3.92	1.201
PU3	150	2	5	3.79	1.119
PU4	150	1	5	4.07	1.043
Valid N (listwise)	150

As regards perceived usefulness, table 4.1 above revealed that respondents use online shopping to a large extent because they perceived that it enables them complete their shopping tasks more quickly (mean of 4.03). Also, the mean result of (3.92) from the respondents indicates that to a large extent the respondents perceived the use of online shopping to make better shopping decisions (3.79). Lastly the respondents perceived online shopping to be useful to a large extent (4.07)

4.4 Factor Analysis

Table 4.5 Correlational Matrix

Correlation Matrix^a
		PP	PR	PU	DV
Correlation	PP	1.000	-.015	-.003	.030
	PR	-.015	1.000	.222	.302
	PU	-.003	.222	1.000	.561
	DV	.030	.302	.561	1.000
Sig. (1-tailed)	PP		.428	.486	.360
	PR	.428		.003	.000
	PU	.486	.003		.000
	DV	.360	.000	.000

a. Determinant = .619

A correlation structure is suitable for a factor analysis only if the inverse forms a diagonal matrix. The matrix is diagonal when the non-diagonal elements are close to zero as possible. There is no generally accepted rule. The inverse of the correlation matrix is essentially a visual aid for testing suitability.

From table 4.5 above the non-diagonal elements are significantly smaller => Correlation structure is well suited fir a factor analysis.

Table 4.6 KMO and Bartlett’s Test

KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		.574
Bartlett’s Test of Sphericity	Approx. Chi-Square	70.433
	df	6
	Sig.	.000

Null hypothesis H₀: The random sample comes from a universe in which all variables are completely uncorrelated.

Alternative hypothesis H₁: The random sample does not come from a universe in which all variables are completely uncorrelated (Kaiser, 1970).

Decision rule: Reject Null hypothesis if the sig value is 0.000

Decision: The null hypothesis was rejected and the alternative hypothesis was accepted. Hence, we conclude that the random sample does not come from a universe in which all variables are completely uncorrelated. Although, the KMO value of 0.574 is still not desirable because the rule of thumb is that KMO should be 0.60 or higher in order to proceed with a factor analysis.

Table 4.7 Communalities

Communalities
	Initial	Extraction
PP	1.000	.991
PR	1.000	.357
PU	1.000	.669
DV	1.000	.732

Extraction Method: Principal Component Analysis.

Table 4.7 presents the result of the communalities. Higher communalities are usually considered to be better and it can be inferred that PP alone has higher communalities with the component. PU and DV has a low to moderate communalities with the component while PR only PR do not relate with other items in the table.

4.5 Reliability test

Table 4.8 Reliability test statistics

Research Instrument	No. of Items	Cronbach’s Alpha
Purchase Behaviours	4	0.880
Perceived Price	4	0.766
Perceived Risk	4	0.881
Perceived Usefulness	4	0.874

Table 4.8 above present the reliability test statistics of each sub-construct used in this study. Specifically, the value of the Cronbach’s alpha as shown in the table revealed that purchase behaviours, perceived risk and perceived usefulness are at least 0.80 and above. This according to Zikmund et al. (2010), can be rightly concluded as excellent good reliability for further analysis. However, the Cronbach’s alpha value for perceived price is 0.7, but it is still able to be concluded as a good reliability instrument for further analysis in this study.

4.6 Report of Demographic Profile

Table 4.9 Distribution of the demographic variables of respondents

Variable	Frequency	Percentage (%)
*Sex*
Male	52	34.7
Female	98	65.3
*Age*
18- 25 years old	47	31.3
26- 35 years old	48	32.0
36 – 45 years old	32	21.3
46- 55 years old	16	10.7
Above 55 years old	7	4.7
*Race*
Malay	73	48.7
Chinese	57	38.0
Indian	17	11.3
Others	3	2.0
*Education*
High School/ Diploma	65	43.3
Bachelor Degree	76	50.7
Master Degree	8	5.3
PhD Degree	1	.7
*Income*
< RM1000	14	9.3
RM1001 – RM2000	63	42.0
RM2001 – RM3000	34	22.7
RM3001 – RM4000	24	16.0
> RM4000	15	10.0
*Online Shopping Frequency*
1-2 times a week	49	32.7
3-4 times a week	40	26.7
5-6 times a week	32	21.3
7-8 times a week	22	14.7
More than 8 times a week	7	4.7
*Most Favourite online shopping platform*
Shopee	57	38.0
Lazada	17	11.3
Taobao	34	22.7
Mudah	17	11.3
Zalora	10	6.7
Lelong	6	4.0
eBay	3	2.0
Amazon	2	1.3
MyFave	3	2.0
Others	1	.7

From the table above in respect to gender, the available data reveals that 52 of our respondents were males, while 98 of our respondents were females. Statistically, this figure implies that 34.7 percent of the respondents are male while the remaining 65.3 percent of the respondents are females. This data is in line with our intuition as it is widely believed that females are mostly saddled with the responsibility of purchasing than males.

In addition, it was revealed that the respondents between the age of 18-25 years old and 26-35 years old shared the greatest percentage of 31.3 and 32.0 respectively. Respondents between the age of 36-45 years also pooled a good percentage of 21.3. While respondents that falls in the age bracket of 46-55 years and above 55 years old represented 10.7 and 4.7 percent of the respondents respectively. As seen from the statistics, it can be inferred that people from within the age group of 18-25 and 26-35 years old constituted majority of the online shopping consumers. The reason behind this may not be farfetched as it can be deduced that consumers within this age bracket have huge tendencies of using internet facilities and mobile services more than the other age group who are likely not to be familiar with the online shopping or may be too sceptical to use it due to their age and conservative ways of doing things.

Moreover, it was revealed that Malaysians with 48.7 percent constituted the larger part of the respondents that partake in online shopping while Chinese also make a greater deal of online shopping by pooling 38.0 percent. Indian is the third main nation with just 11.3 percent while other countries represented just 2.0 percent of the whole respondents. On the other hand, it was revealed from the statistics that more than half of the respondents that are sampled as regards online shopping behaviour of consumers are Bachelor Degree holder with the frequency of 76 (50.7%). The second highest qualification of the respondent were those with High school/Diploma degree with the frequency of 65 and it constitute 43.3%, followed by Master Degree holders with frequency of 8(5.3% ) and the least of the respondent was PhD Degree holders with only 1 respondent and it constituted less than 1 percent (0.7%).

Apart from that, the financial strength of all the respondents that are sampled for this work was also investigated in order to have an insight of how income of consumers influences their online shopping behaviour. To achieve this, the respondents’ income was categorized into five (i) < RM1000 (ii) RM1001 – RM2000. (iii) RM2001 – RM3000 (iv) RM3001 – RM4000 (v) > RM4000. In addition, it was revealed that 63 of the respondents are earning the average income of RM1001 – RM2000 annually. This figure represented 42.0 percent of all the respondents. Also, the respondents whose income falls under RM2001 – RM3000 annually has the second highest frequency of 34 which constitutes 22.7 percent of the total respondents. RM3001 – RM4000 income earners within the range of RM3001 – RM4000 are just 24. This figure was estimated to be 16 percent of the total respondents. There is a slight difference between the respondents that earned the lowest and highest income of less than RM1000 and above RM1000. The former being 14 while the latter is 15. This was estimated to be 9.3 and 10.0 percent respectively.

In fact, the results presented in table 4.9.5 revealed that respondents’ Online Shopping Frequency. The table shows that 49 (32.7 percent) of the respondents’ shops online in 1-2 times a week. Also, 40 (26.7 percent) of the total respondents’ shops online in 3-4. This was followed by 32 (21.3 percent) of the total respondents that also shop online in 5-6 times a week. Only 22 (14.7 percent) of the total respondents’ shops times in 7-8 times a week. The remaining 7 (4.7 percent) respondents are those that shops more than 8 times a week. Besides that, the findings from the table above sets out the Favourite online shopping platform of respondents. It was revealed that Shopee was mostly preferred by 57 (38.0 percent) of the respondents followed by Taobao with 34 (22.7 percent) of the respondents claiming it to be their favourite online shopping platform. Lazada and Mudah each have 17(11.3 percent) of the respondents. 10 (6.7 percent) of the respondents revealed zalora has their favourite online shopping platform while 6 (4.0 percent) of the respondents also also have Lelong as their favourite shopping platform. ebay and MyFave each have three of the total respondents while Amazon and the online shopping platform has 2 (1.3 percent and 1 (0.7) percent respectively.

4.7 Assumption of Multiple Regression

4.7.1 Normality test

Figure 4.1 Normal P-P Plot

From the Normal P-P plot, all the points are positioned in a reasonably straight diagonal line from bottom left to top right. Although, there are some points that skew right off from the straight diagonal line, overall, there is no indication of a major deviation from normality. Therefore, the normality result is appropriate and acceptable.

Figure 4.2 Scatterplot

The scatter plots in figure 4.2 revealed that there is a slight negative correlation between the variables. In addition, the scatter plot of the residual in Figure 4.2 shows that the residuals are roughly distributed rectangularly. The findings demonstrate that the predictors (independent variables) are linearly related to the residual of the criterion (dependent variables). Therefore, the homoscedasticity of the sample for this study is ensured. In addition, there is no sign of obvious outliers

4.7.2 Multicollinearity test

Table 4.10 Multicollinearity Test (VIF)

Model		Collinearity Statistics
Model		Tolerance	VIF
1	(Constant)
	PP	1.000	1.000
	PR	.950	1.052
	PU	.951	1.052

From table 4.72 above, the result of the VIF is lesser than significantly less than 2 and that of Tolerance is lesser than 1. This implies that there is enough evidence to statistically conclude that there is absence of multicollinearity in the model.

4.7.3 Linearity Test

Table 4.11 Linearity Test

Correlations
		PP	PR	PU	DV
PP	Pearson Correlation	1
PP	Sig. (2-tailed)
PR	Pearson Correlation	-.015	1
PR	Sig. (2-tailed)	.856
PU	Pearson Correlation	-.003	.222^**	1
PU	Sig. (2-tailed)	.973	.006
DV	Pearson Correlation	.030	.302^**	.561^**	1
DV	Sig. (2-tailed)	.720	.000	.000
**. Correlation is significant at the 0.01 level (2-tailed).

From the Table 4.12, it is obvious that none of the variables are highly correlated with any other variable as shown in the correlation matrix table above. Given that all the correlation values are well below the threshold of 0.9, we could then conclude that there is no problem of linearity among the variables under investigation.

4.8 Multiple Regression

4.8.1 Model Summary

Table 4.12 Model Summary

Model Summary^b
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.591^a	.349	.335	.67365

a. Predictors: (Constant), PU, PP, PR

b. Dependent Variable: DV

Table 4.13 above reveals the summary of the model used for this study. From the summary above, the R squared which is the coefficient of determination is 0.591. This simply implies that 59.8 percent of the total variation in the factors affecting an online shopping behaviour of consumers can be jointly explained by the independent variables ( perceived price, perceived risk and perceived usefulness).

4.8.2 ANOVA Table

Table 4.13 ANOVA table

ANOVA^a
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	35.484	3	11.828	26.064	.000^b
	Residual	66.256	146	.454
	Total	101.740	149

Table 4.14 above shows that the three explanatory variables perceived price, perceived risk and perceived usefulness) are not all equal to each other and could be used to determine the dependent variable, as shown by an F-value of 26.06 and significance level of 0.00 (p<0.05).

4.8.3 Coefficient Table

Table 4.14 Coefficient Table

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
Model		B	Std. Error	Beta	t	Sig.
1	(Constant)	1.547	.428		3.616	.000
	PP	.039	.077	.034	.505	.614
	PR	.173	.064	.187	2.726	.007
	PU	.463	.061	.520	7.586	.000

Table 4.15 above revealed that there exists a positive relationship between the dependent and independent variables. What this result implies is that a unit increase in perceived price will result to a 0.034 percent increase in online shopping ceteris paribus. Also, holding all other explanatory variables fixed, a unit increase in perceived risk is estimated to increase online shopping by 0.187 percent While a unit increase in perceived usefulness by the consumers will result to an 0.520 percent increase in online shopping. Of all the coefficients, it is worthy to point out that only perceived price which is expected to be significant turns out to be insignificant.

4.9 Summary

The chapter employed both descriptive and inferential statics to present results, analyse and interpret it in a concise manner. All the performed analyses were extensively discussed in this chapter. The normality tests indicate that the sample is normally distributed. The factor analysis and Cronbach’s alpha coefficient test confirm the validity and reliability of the instrument adopted in this study. From the descriptive statistics, it was revealed from the frequency distribution of the respondents, it was revealed that youths mostly in the age of 18 to 35 years are the most online shopping customers. The result from the multiple regression analysis indicated that all the three explanatory variables exact positive impact on the dependent variables. Although, not all are in line with the A priori expectations. The total variation of dependent variable explained by explained by the independent variables in this model is 59.1%.

5 chapter 5: conclusion and recommendations

5.0 Introduction

This chapter presents all logical conclusions derived from the findings and make suggestions and recommendation based on the researcher’s findings on the main objective of this study which is to examine the determinants that impact on the online shopping behaviours of consumers The first part of this chapter addresses discussion on the objectives of the study from the findings, the second part of this chapter focused on the practical management implication of the study while the last part addresses the recommendation policies. Suggestion for further study shall also be pointed out.

5.1 Discussion of findings

Research findings from this study revealed that all the 150 males and females that constitute our sample, females are mostly saddled with the responsibility of purchasing than males this can logically acceptable to a reasonable extent. Also, it was found from the age distribution of the respondents that people from within the age group of 18-25 and 26-35 years old constituted majority of the online shopping consumers. The reason behind this may not be farfetched as it can be deduced that consumers within this age bracket have huge tendencies of using internet facilities and mobile services more than the other age group who are likely not to be familiar with the online shopping or may be too sceptical to use it due to their age and conservative ways of doing things.

5.1.1 What is the influence of Perceived price factors on the online shopping behaviours of the consumers?

The findings from the preceding chapter of the study revealed that perceived price factor although statistically insignificant @ 5% has a positive influence on the online shopping behaviours of the consumers. What this implies is that holding all other variables constant, a unit increase in perceived price factor will positively influence online shopping behaviours of the consumers by 0.034 percent. This can simply be explained by the theory of demand and substitution effect of utility. The finding is in line with the study of (Al-Salamin & Al-Hassan, 2016), which was primarily aimed at measuring the impact of pricing strategies on consumers’ psychology and on their buying behaviour accordingly. The duo found that there is a positive relationship between perceived prices and consumer online shopping buying behaviour. Also, the findings of the study are supported by the findings of (Elida, Rahardjo, Raharjo, & Sukirman, 2019) where they found that price and some other variables are the significantly influencing consumers in online shopping. However, the P-value of the coefficient is statistically insignificant @ 5% and 10% level.

5.1.2 What is the influence of perceived risk on the online shopping behaviours of the consumers?

The findings from the regression analysis in the preceding chapter of the study shows that perceived risk has a positive and statistically significant influence on the online shopping behaviours of the consumers. This finding is in line with the findings of (Mamman, Maidawa, & Saleh, 2015) who found from the results of simple regression that that financial risk has no effect on online shopping. Also, the finding contradicts the result from existing literature of (Mudaa, Mohdb, & Hassan, 2016), who submitted that of the four factors proposed in the model (perceived risk, perceived reputation, perceived reputation and perceived usefulness), only perceived trust and perceived reputation have a significant positive relationship with online purchase intention of Gen Y shoppers. Although, this finding contradicts with Apriori expectations, it also goes against lots of the findings from existing literatures. However, this study is not supported by the study of (Bertea, 2010), who found that perceived risk is one of the most important barriers which prevent the adoption of e-commerce as a new shopping channel.

5.1.3 What is the influence of perceived usefulness on the online shopping behaviours of the consumers?

The findings from the regression analysis in the preceding chapter of the study revealed that perceived usefulness has a positive and statistically significant influence on the online shopping behaviours of the consumers. Specifically, this implies that a unit increase in perceived usefulness will influence a positive increase in online shopping behaviours of the consumers by 0.520 percent all other things being equal. This finding is supported by A priori expectations and it is also backed by some existing literatures. The study supports the existing findings of (Alan, Kabadayi, Bakis, & Ildokuz, 2017), who found that, consumer perceived usefulness has the strongest effect on consumer’s purchase intention. Also, this study is in line with the findings of (Jin, Osman, & AB.Halim, 2014), who submitted that Perceived usefulness and trust are predicted to influence the online shopping behaviours of consumers in northern Malaysia since these two factors were proven to influence the online shopping behaviour of other developing countries such as China. Also, this findings is supported by the submission of (Sin, Nor, & Al-Agaga, 2012), who found that perceived usefulness was the most dominant factors that influence young consumers’ online purchase intention through social media, followed by perceived ease of use and subjective norm.

5.2 Limitation of the study

A research work of this nature can never be achieved without any form of limitations during the course of studies. The major limitation faced in the course of this research work are the followings.

5.2.1 Low Response

This limitation was encountered during data collection using questionnaire. Some respondents failed to give clear or complete answers and this led to complication in the analysis while other were not co-operative being feared to provide information needed thinking that it might affect them and organization principles.

5.2.2 Time Span

Time constraints also act as one of the key limitations faced in the course of this research. The researcher being a full-time student offering other courses with research work was highly tedious. Aside being tedious, it’s really not easy to productively progress at a faster pace and targeted time was extended over and over.

5.2.3 Insufficient sample size

The sample size for this study was intended to accommodate lots of respondents. However, there attitude towards the research and their unwillingness in responding forced the researcher to limit the sample size to just 150 which seems so small for a study of this type given the rate at which people embarked on online shopping.

5.3 Theoretical implication of the study

This research work has contributed to existing theories and existing literature in order to improve consumers’ online shopping behaviour. It makes significant contribution towards examining the factors that could influence the behaviour of the consumers as regards online shopping. Few studies that have been previously done as regards this topic did not employ some robust and sophisticated methods of analysis as used in this study.

In the scenario of online shopping, consumers may develop low trust and high risk perception due to lack of actual interaction with the product and lack of any sensory interaction with it. Obtaining better deals and better bargains as compared to physical shopping may reduce this type of risk among the potential buyers over online medium.

The findings from our analysis validates most existing theories which can be concluded that the explanatory variables that are used for this research are relevant. Although, there exists some findings that are not supported by existing literatures and also contradicts the A priori expectations.

5.4 Practical implication of the study

Doing everything possible to improve consumers’ online shopping behaviour shouldn’t be overemphasized. This study have carefully identified and conceptualized the factors that are considered helpful for this sake. Perceived price, perceived risk and perceived usefulness are the factors deemed to have influence on the consumers’ online shopping behaviour. These phenomena will directly give the implication towards the consumer’s behaviour in doing the online purchase.

In relation to perceived risk, customers’ will continue to avert online shopping because, they are still concerned about the financial loss that they may incur if purchased product did not perform as expected or if the product delivered refused to work or failed to meet their need.

The finding from this study offers an insight for practical policies to be formulated and implemented in order to have an effective and efficient online shopping that will be capable of meeting the consumers’ demand. The findings and discussions in this article can be used as guidelines for businesses and individuals who plan to conduct online business through social media. Furthermore, there are several recommendations provided for the purpose of further studies in this topic.

5.5 Recommendations for future study

The importance of knowing the factors that influence the relationship and impact of communicable diseases on Malaysian health can never be overemphasized. This study thus recommended the following policies based on the findings from the results:

5.5.1 Time Span

A research work of this nature requires time and total concentration of the researcher. Attending to it and any other academic activities is a sort period of time may be unproductive. To be able to undertake an appropriate search that will be short of errors, the researcher recommends that enough time should be allocated to students that are doing this kind of research as it is still a new area of research.

5.5.2 The data collection Methods

The method of data collection for study of this nature is recommended to be through online survey designed in a very convenient and time saving manners in order to have lots of respondents that will be willing to answer the questions. Also, it should be structured in such a way that it will capture the targeted respondents rather than just sampling everyone as respondents.

5.5.3 Sufficient Sample size

To have a study worthy of policy recommendation, it is suggested that future researchers set a larger sample size of respondents in order to acquire more reliable information and ensure generalizability of the findings. It is only in this regards that lots of opinion cable of reshaping online shopping behaviours can be gathered

5.6 Conclusion

The objectives of the study were to examine into the influence of price factors on the online shopping behaviours of the consumers, examine into the influence of perceived risk on the online shopping behaviours of the consumers and to examine into the influence of perceived usefulness on the online shopping behaviours of the consumers. All the objectives are achieved. The main findings of this research found that females used online shopping more than males and the youths are more comfortable shopping online than the old ones. Observed limitations that can mitigate the purpose of embarking on this study have been identified for further recommendation.

References

Alan, A. K., Kabadayi, E. T., Bakis, S., & Ildokuz, S. I. (2017). The Role Of Individual Factors On OnlineShopping Behavior. Journal of Business, Economics and Finance (JBEF), 6(3), 200-215.

Al-Ghaith, W., Sanzogni, L., & Sandhu, K. (2010). Factors influencing the adoption and usage of online services in Saudi Arabia. EJISDC: The Electronic Journal on Information Systems in Developing Countries, (40), 1.

Al-Salamin, H., & Al-Hassan, E. (2016). The Impact of Pricing on Consumer Buying Behavior in Saudi:Arabia : Al-Hassa Case Study. European Journal of Business and Management, 8(12).

Aliaga, M. and Gunderson, B. (2002). Interactive Statistics. [Thousand Oaks]: Sage Publications.

Al-Smadi, M.O. 2012 ‘Factors Affecting adoption of electronic banking: an analysis of the perspectives of banks’ customers’, International Journal of Business and Social Science, 3(17), 294 – 309.

Andrews, C. B. L. (2012). Risk, trust, and consumer online purchasing behaviour: a Chilean perspective. International Marketing Review, 29(3) pp. 253 – 275.

Balogh, Z. and Mészáros, K. 2020 ‘Consumer Perceived Risk by Online Purchasing: The Experiences in Hungary’, Naše gospodarstvo/Our Economy, 66(3), 14 – 21. DOI: 10.2478/ ngoe-2020-0014.

Bertea, P. (2010). Perceived Risk And Online Shopping Behaviour Ehaviour.

Bird, D., & Dominey-Howes, D. (2008). Testing the use of a ‘questionnaire survey instrument’to investigate public perceptions of tsunami hazard and risk in Sydney, Australia. Natural Hazards, 45(1), pp. 99-122.

Bradburn, N. and Sudman, S. (2011). The current status of questionnaire design. The measurement errors in surveys, New York: Wiley, pp. 29-40.

Cheema, U., Rizwan, M., Jalal, R., Durrani, F. and Sohail, N. 2007 ‘The trend of online shopping in 21^st century: impact of enjoyment in tam model’, Asian Journal of Empirical Research, 3(2):131-141.

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences. (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.

Dai, B., Forsythe, S., & Kwon, W. S. (2014). Impact of online shopping experience on risk perceptions and online purchase intentions: does product category matter? Journal of Electronic Commerce Research, 15(1), 13–24.

Department of Statistics Malaysia. (2020). Demographic Statistics by State, Third Quarter 2020, Malaysia. Retrieved December 3, 2020, from https://www.dosm.gov.my/v1/index.php?r=column/cthemeByCat&cat=430&bul_id=YUttRnlGZ2VXSkg0M0F2ZHZFTE9IUT09&menu_id=L0pheU43NWJwRWVSZklWdzQ4TlhUUT09#

Devkishin, K.R., Rizvi, A.H. and Akre, V.L. 2013 ‘Analysis of factors affecting the online shopping behavior of consumers in U.A.E.’, pg. 1-6.

Elida, T., Rahardjo, W., Raharjo, A., & Sukirman, E. (2019, May-June). Online Shopping: What Factors Determine Consumers to Buy? Management Studies, 7(3), 238-246.

Eugenia, E. 2015 ‘Examining the perception of Ghanaian online users towards online advertisement and its impact on purchase behaviour: case of social media users’, pg. 1-72.

Forte, D., (2015). Why Customers Prefer Online Shopping. Available at: http://multichannelmerchant.com/infographics/why-customers-prefer-online-shopping06012015/ [Accessed 3 December 2020].

Fram, E. H., & Grady, D. B. (1995). Internet buyers: Will the surfers become buyers? DIRECT MARKETING-GARDEN CITY-, 58, 63-63.

Ganapathi, R. 2015 ‘A study on factors affecting online shopping behavior of consumers in Chennai’, Journal of Management Research and Analysis, 2(2), 123-126.

Gyamfi, G.D. (2014). Evaluating Entrepreneurship Education as a Tool for Economic Growth: The Ghanaian Experience, British Journal of Education, Society & Behavioural Science , Vol. 4(3), 318-335

Hanson, D., & Grimmer, M. (2007). The mix of qualitative and quantitative research in major marketing journals, 1993-2002. European Journal of Marketing, 41(1/2), 58-70.

Hardy, M. & Bryman A. (Eds.)(2004). Handbook of Data Analysis. In Forum Qualitative Sozialforschung/Forum: Qualitative Social Research (Vol. 7, No. 2).

Hawkins, M. (1997). Social Darwinism in European and American thought, 1860- 1945: nature as model and nature as threat. Cambridge University Press.

Hootsuite and Wearesocial, 2020. Digital 2020: Malaysia. Malaysia. Available through: https://datareportal.com/reports/digital-2020-malaysia [Accessed 3 December 2020].

Hsu, M. H., Chuang, L. W., & Hsu, C. S. (2014). Understanding online shopping intention: the roles of four types of trust and their antecedents.Internet Research, 24(3), 332-352.

Hu, L. T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological methods, 3(4), 424.

î Proceedings of the 33rd Hawaii International Conference on System Sciences 2000.

Iriani, S.S. and Andjarwati, A.L. 2020 ‘Analysis of perceived usefulness, perceived ease of use, and perceived risk toward online shopping in the era of covid-19 pandemic’, Systematic Review Pharmacy, 11(12), 313 – 320.

Issac, S., & Michael, W. (1995). Handbook in research and evaluation: A collection of principles, methods, and strategies useful in the planning, design, and evaluation of studies in education and the behavioral sciences. CA: Edits Pub. San Diego.

Jin, L. Y., Osman, A. B., & AB.Halim, M. S. (2014). Perceived Usefulness And Trust Towards Consumer Behaviors: A Perspective Of Consumer Online Shopping. Journal of Asian Scientific Research, 4(10), 541-546

Juniwati (2014). Influence of Perceived Usefulness, Ease of Use, Risk on Attitude and Intention to Shop Online. European Journal of Business and Management, 6(27), pp.218- 228.

Kaiser, H. (1970, December). A second generation little jiffy. 35(4), 401-415.

Kim, H. (2019). Malaysian spend almost a quarter on their day on social media. Digital & technology. Available through: https://my.yougov.com/en-my/news/2019/04/30/malaysians-spend-almost-quarter-their-day-social-m/ [Accessed 3 December 2020]

Kumar., V. and Dange., U. (2012). A study of Factors Affecting Online Buying Behaviour: A Conceptual Model. SSRN Electronic Journal.

Leedy, P. (1997). Practical Research: Planning and Design. Upper Saddle River: N.J.Prentice Hall.

Li, N. and Zhang, P. (2002). Consumer Online Shopping Attitudes and Behaviour: An Assessment of Research. Eighth Americas Conference on Information System. pp. 508-517.

Liang, T., and Lai, H. (2000). ìElectronic store design and consumer choice: an empirical study,

Liao, Z., & Cheung, M. T. (2001). Internet-based e-shopping and consumer attitudes: an empirical study. Information & Management, 38(5), 299-306.

Limbu, Y. B., Wolf, M., & Lunsford, D. (2012). Perceived ethics of online retailers and consumer behavioral intentions: The mediating roles of trust and attitude. Journal of Research in Interactive Marketing, 6(2), 133-154.

Mamman, H., Maidawa, M., & Saleh, M. (2015). Effects of Perceived Risk on Online Shopping. Proceedings of the 1st Management, Technology, and Development Conference. Bauchi.

Malhotra, N. K., & Birks, D. F. (2007). Marketing research: An applied approach.

Mat, N.K. and Sentosa, I. 2008 ‘The integration of theory of planned behavior (TPB) and technology acceptance model in internet purchasing: a structural equation modeling (SEM) approach’, Proceedings of Applied International Business Conference, pg. 466 – 481.

Maziriri, E.T. and Chuchu, T. 2017 ‘The conception of consumer perceived risk towards online purchases of apparel and an idiosyncratic scrutiny of perceived social risk: a review of literature’, International Review of Management and Marketing, 7(3), 257-265.

Mazzini, M., Rohani, M., and Salwana. (2016). Online Purchase Behavior of Generation Y in Malaysia. Procedia Economics and Finance, 37(July), pp. 292–298.

Md Shah, A., Safri, S. N. A., Thevadas, R. and Noordin, N.K. (2020). COVID-19 Outbreak in Malaysia: Actions Taken by the Malaysian Government. International Journal of Infectious Diseases.

Mudaa, M., Mohdb, R., & Hassan, S. (2016). Online Purchase Behavior of Generation Y in Malaysia. Procedia Economics and Finance, 37, pp. 292 – 298.

Neuman, W. L. (2006). Social Research Methods:Qualitative & Quantitative Approaches, (6thEds). Boston: Pearson/ Allyn & Bacon.

Pearson Education.

Pera, S. A., and Tonder, S. V. (2005). Ethics in health care. Lansdowne South Africa: Juta.

Punj, G. (2011). Impulse buying and variety seeking: Similarities and differences. Journal of Business Research, 64(7), 745-748.

Saunders, M., Lewis, P. and Thornhill, A. (2009). Research Method for business students. England: Pearson education limited Harlow.

Schafer, J. L., & Graham, J. W. (2002). Missing data: our view of the state of the art. Psychological methods, 7(2), 147.

Schindler, R. & Bickart, B. (2012). Perceived helpfulness of online consumer reviews: The role of message content and style. Journal Of Consumer Behaviour, 11(3), 234-243.

Schumacker, R. E., & Lomax, R. G. (2004). A beginner’s guide to structural equation modeling. Psychology Press.

Sekaran, U., & Bougie, R. (2012). Research methods for business: A skill building. Chichester, West Sussex: John Wiley & Sons.

Sin, S. S., Nor, K. M., & Al-Agaga, A. M. (2012). Factors Affecting Malaysian young consumers’ online purchase intention in social media websites. Procedia – Social and Behavioral Sciences, 40, 326 – 333.

Sirrka L. Jarvenpaa, Peter A. Todd (1996). Consumer Reactions to Electronic Shopping on the World Wide Web. International Journal of Electronic Commerce, 1(2), 59-88.

Stephanie. (2014). Sampling frame/ sampling frame definition. Retrieved December 3, 2020, from https://www.statisticshowto.datasciencecentral.com/samplingframe/

Sunitha C.K., and Gnanadhas, E. (2014). Online Shopping- An Overview. B-Digest, 6, pp. 16-22.

Surabaya, S. S. and Andjarwati, A. L. (2020). Analysis of Perceived Usefulness, Perceived Ease of Use, and Perceived Risk toward Online Shopping in the era of COVID-19 Pandemic. Sys Rev Pharm 2020, 11(12), pp. 313-320.

Surendran, P. 2012 ‘Technology Acceptance Model: A Survey of Literature’, International Journal of Business and Social Research (IJBSR), 2(4), 175 – 178.

Suruhanjaya Komunikasi dan Multimedia Malaysia. (2018). Internet Users Survey 2018. Malaysian Communications and Multimedia Commission. Malaysia, pp. 1–39.

Tabachnick, B. G., & Fidell, L. S. (2007). Experimental designs using ANOVA. Thomson/Brooks/Cole.

Teodorescu, N., Pop, N.A. and Stăncioiu, A.F. 2008 ‘Price determination and price strategy in the marketing view’, Management & Marketing, 3(4), 21-36.

Thamizhvanan, A. & Xavier, M. (2013). Determinants of customers’ online purchase intention: an empirical study in India. Jnl Of Indian Business Res, 5(1), 17-32.

Toñita P. Y. M. B. G.C. D. K. D. R., (2004). What drives consumers to shop online? A literature review. International Journal of Service Industry Management, 15(1), pp. 102 – 121.

Ujwala D. and Vinay K. 2012 ‘Study of factors affecting online buying behavior: a conceptual model’, pg. 1-11.

Utz, S., Kerkhof, P., & van den Bos, J. (2012). Consumers rule: How consumer reviews influence perceived trustworthiness of online stores. Electronic Commerce Research And Applications, 11(1), 49-58.

Wai, K., Dastane, O., Johari, Z., and Ismail, N. B. (2019). Perceived risk factors affecting consumers’ online shopping behaviour. The Journal of Asian Finance, Economics and Business, 6(4), pp. 246-260.

Wekeza, S.V.and Sibanda, M. (2019). Factors Influencing Consumer Purchase Intentions of Organically Grown Products in Shelly Centre, Port Shepstone, South Africa. International Journal of Environmental Research and Public Health 2019, 16(6), pp. 956.

Worldometer. (2020). Countries where COVID-19 has spread. Retrieved December 3, 2020, from https://www.worldometers.info/coronavirus/countries-where-coronavirus-has-spread/ [Accessed 3 December 2020]

Zhou, L., Liwei, D. and Dongsong, Z. (2007). Online shopping acceptance model- a critical survey of consumer factors in online shopping. Journal of Electronic Commerce Research, 8(1), pp. 41-42.

Zikmund, W. G. (2003). Sample designs and sampling procedures. Business research methods, 7(2), South-Western: Cengage Learning. pp. 368-400.

Zikmund, W. G., Babin, B. J., Carr, J. C., and Griffin, M. (2013). Business Research Methods (9th Edition). South-Western: Cengage Learning.

Zikmund, W., Babin, B., Carr, J., & Griffin, M. (2010). Business Research Methods. South-Western: Cengage Learning.

APPENDICES

G power sampling size

Appendix 3.6: Determining the sample size by using G-Test

Survey Questionnaire

Research Title: Factors affecting online shopping behaviours of consumers

Dear Respondent,

The questionnaire is part of my Final Year Project endeavour in partial fulfillment of the requirement of Bachelor of business (hons) courses -UBBUS from UOWMKDU university college. The purpose of this research is to examine the factors affecting online shopping behaviours of consumers

Your response to the questionnaire will provide a significant contribution to the final result of this research. To ensure valid and reliable findings, your kind of help is very much required. Hence, this questionnaire is targeted for the consumers, especially those who are actively engaged in online shopping, regarding to their online buying behaviours towards various online shopping networks nowadays.

This questionnaire has been divided into two sections, which are section A, section B and section C. Kindly answer the entire questionnaire without leaving any blank. All the information and answers that you provided will be kept strictly confidential and used for academic purpose in this study only. Thank you for your high cooperation and I will truly appreciate it.

Sincerely,

Researcher’s name: pheona Davie kanyoza

Researcher’s email 0123764@kdu-online.com

Before participating in this study, please kindly read the instruction below to demonstrate your understanding on this study.

I agree that I have fully read the instructions stated above and ready to participate in this study with my full knowledge of understanding.

Section A: Demographic Information

Please Tick (√) your answer on the blank boxes provided.

Gender

Male
Female

18 – 25 years old
26 – 35 years old
36 – 45 years old
46 – 55 years old
Above 55 years old

Race

Malay
Chinese
Indian
Others, please specify: _____________

What is your Current Education Level?

High School / Diploma
Bachelor / Degree
Master Degree
D Degree
Others

What is Your Current Income Level?

< RM 1000
RM 1001- RM2000
RM 2001-RM3000
RM 3001-RM4000
> RM4000

Kindly select ONE answer that most fits your frequency of shopping in online platform each week.

None
1-2 times a week
3-4 times a week
5-6 times a week
7-8 times a week
More than 8 times a week

Which of the following existing shopping platform listed below is your most favourite online shopping platform in Malaysia?

Shopee
Lazada
Taobao
Mudah
Zalora
Lelong
eBay
Amazon
MyFave
Others

Section B: Independent Variable

Independent Variables	Questionnaire Elements	References
Perceived Price	1. I feel that online shopping would save money	(Maiyaki, 2016)
	2. I feel that the price offered at online store is consistent
	3. I think the product’s price labelling in online shopping is clear
	4. In online shopping, a higher price of a product will result in better quality of the product.
Perceived Risk	1. I might not get what I ordered through online shopping	(Vaghela, 2017)
	2. It is hard to judge the quality of merchandise online
	3. I feel that my credit card details may be compromised and misused if I shop online
	4. I might not receive the product ordered online.
Perceived Usefulness	1. Using the Internet enables me to finish my shopping tasks more quickly	(Vaghela, 2017)
	2. Using the Internet for shopping helps me to make better purchase decisions
	3. Using the Internet makes it easier to make purchases
	4. Overall, I find using the Internet for shopping useful

Section C: Dependent Variable

Variable	Questionnaire Elements	References
Online Consumers’ Shopping Behaviours	1. I should consider using online shopping rather than traditional shopping	(Maiyaki, 2016)
	2. I should consider using online shopping as often as possible
	3. I intend to shop online in the near future
	4. I will definitely shop online in the future

SPSS Results

FREQUENCIES VARIABLES=Consent_Participation Gender Age Race Education Income

Online_Shopping_Frequency Most_Favourite_online_shopping_platform

/PIECHART FREQ

/ORDER=ANALYSIS.

Frequencies

Statistics
		Consent_Participation	Gender	Age	Race	Education	Income
N	Valid	150	150	150	150	150	150
N	Missing	0	0	0	0	0	0

Frequency Table

Consent_Participation
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Yes	150	100.0	100.0	100.0

Gender
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Male	52	34.7	34.7	34.7
	Female	98	65.3	65.3	100.0
	Total	150	100.0	100.0

Age
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	18- 25 years old	47	31.3	31.3	31.3
	26- 35 years old	48	32.0	32.0	63.3
	36 – 45 years old	32	21.3	21.3	84.7
	46- 55 years old	16	10.7	10.7	95.3
	Above 55 years old	7	4.7	4.7	100.0
	Total	150	100.0	100.0

Race
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Malay	73	48.7	48.7	48.7
	Chinese	57	38.0	38.0	86.7
	Indian	17	11.3	11.3	98.0
	Others	3	2.0	2.0	100.0
	Total	150	100.0	100.0

Education
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	High School/ Diploma	65	43.3	43.3	43.3
	Bachelor Degree	76	50.7	50.7	94.0
	Master Degree	8	5.3	5.3	99.3
	PhD Degree	1	.7	.7	100.0
	Total	150	100.0	100.0

Income
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	< RM1000	14	9.3	9.3	9.3
	RM1001 – RM2000	63	42.0	42.0	51.3
	RM2001 – RM3000	34	22.7	22.7	74.0
	RM3001 – RM4000	24	16.0	16.0	90.0
	> RM4000	15	10.0	10.0	100.0
	Total	150	100.0	100.0

Online_Shopping_Frequency
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	1-2 times a week	49	32.7	32.7	32.7
	3-4 times a week	40	26.7	26.7	59.3
	5-6 times a week	32	21.3	21.3	80.7
	7-8 times a week	22	14.7	14.7	95.3
	More than 8 times a week	7	4.7	4.7	100.0
	Total	150	100.0	100.0

Most_Favourite_online_shopping_platform
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Shopee	57	38.0	38.0	38.0
	Lazada	17	11.3	11.3	49.3
	Taobao	34	22.7	22.7	72.0
	Mudah	17	11.3	11.3	83.3
	Zalora	10	6.7	6.7	90.0
	Lelong	6	4.0	4.0	94.0
	eBay	3	2.0	2.0	96.0
	Amazon	2	1.3	1.3	97.3
	MyFave	3	2.0	2.0	99.3
	Others	1	.7	.7	100.0
	Total	150	100.0	100.0

Pie Chart

FACTOR

/VARIABLES PP PR PU DV

/MISSING LISTWISE

/ANALYSIS PP PR PU DV

/PRINT INITIAL CORRELATION SIG DET KMO INV REPR AIC EXTRACTION

/CRITERIA MINEIGEN(1) ITERATE(25)

/EXTRACTION PC

/ROTATION NOROTATE

/METHOD=CORRELATION.

Factor Analysis

Correlation Matrix^a
		PP	PR	PU	DV
Correlation	PP	1.000	-.015	-.003	.030
	PR	-.015	1.000	.222	.302
	PU	-.003	.222	1.000	.561
	DV	.030	.302	.561	1.000
Sig. (1-tailed)	PP		.428	.486	.360
	PR	.428		.003	.000
	PU	.486	.003		.000
	DV	.360	.000	.000

a. Determinant = .619

KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		.574
Bartlett’s Test of Sphericity	Approx. Chi-Square	70.433
	df	6
	Sig.	.000

Communalities
	Initial	Extraction
PP	1.000	.991
PR	1.000	.357
PU	1.000	.669
DV	1.000	.732

Extraction Method: Principal Component Analysis.

DESCRIPTIVES VARIABLES=PP1 PP2 PP3 PP4

/STATISTICS=MEAN STDDEV MIN MAX.

Descriptives

Descriptive Statistics
	N	Minimum	Maximum	Mean	Std. Deviation
PP1	150	1	5	3.85	.932
PP2	150	1	5	3.73	.960
PP3	150	1	5	3.68	.907
PP4	150	1	5	3.61	.947
Valid N (listwise)	150

DESCRIPTIVES VARIABLES=PR1 PR2 PR3 PR4

/STATISTICS=MEAN STDDEV MIN MAX.

Descriptives

Descriptive Statistics
	N	Minimum	Maximum	Mean	Std. Deviation
PR1	150	1	5	4.05	1.035
PR2	150	1	5	4.05	.982
PR3	150	1	5	3.90	1.073
PR4	150	1	5	4.06	1.057
Valid N (listwise)	150

DESCRIPTIVES VARIABLES=PU1 PU2 PU3 PU4

/STATISTICS=MEAN STDDEV MIN MAX.

Descriptives

Descriptive Statistics
	N	Minimum	Maximum	Mean	Std. Deviation
PU1	150	2	5	4.03	.972
PU2	150	1	5	3.92	1.201
PU3	150	2	5	3.79	1.119
PU4	150	1	5	4.07	1.043
Valid N (listwise)	150

DESCRIPTIVES VARIABLES=DV1 DV2 DV3 DV4

/STATISTICS=MEAN STDDEV MIN MAX.

Descriptives

Descriptive Statistics
	N	Minimum	Maximum	Mean	Std. Deviation
DV1	150	1	5	4.17	1.006
DV2	150	2	5	4.27	.946
DV3	150	1	5	4.25	.921
DV4	149	2	5	4.19	.982
Valid N (listwise)	149

REGRESSION

/MISSING LISTWISE

/STATISTICS COEFF OUTS R ANOVA COLLIN TOL

/CRITERIA=PIN(.05) POUT(.10)

/NOORIGIN

/DEPENDENT DV

/METHOD=ENTER PP PR PU

/SCATTERPLOT=(*ZRESID ,*ZPRED)

/RESIDUALS HISTOGRAM(ZRESID) NORMPROB(ZRESID).

Regression

Variables Entered/Removed^a
Model	Variables Entered	Variables Removed	Method
1	PU, PP, PR^b	.	Enter

a. Dependent Variable: DV

b. All requested variables entered.

Model Summary^b
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.591^a	.349	.335	.67365

a. Predictors: (Constant), PU, PP, PR

b. Dependent Variable: DV

ANOVA^a
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	35.484	3	11.828	26.064	.000^b
	Residual	66.256	146	.454
	Total	101.740	149

a. Dependent Variable: DV

b. Predictors: (Constant), PU, PP, PR

Coefficients^a
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.	Collinearity Statistics
Model		B	Std. Error	Beta	t	Sig.	Tolerance
1	(Constant)	1.547	.428		3.616	.000
	PP	.039	.077	.034	.505	.614	1.000
	PR	.173	.064	.187	2.726	.007	.950
	PU	.463	.061	.520	7.586	.000	.951

Collinearity Diagnostics^a
Model	Dimension	Eigenvalue	Condition Index	Variance Proportions
Model	Dimension	Eigenvalue	Condition Index	(Constant)	PP	PR	PU
1	1	3.904	1.000	.00	.00	.00	.00
	2	.046	9.245	.01	.42	.10	.36
	3	.038	10.135	.00	.02	.69	.49
	4	.012	17.999	.99	.55	.21	.14

a. Dependent Variable: DV

Residuals Statistics^a
	Minimum	Maximum	Mean	Std. Deviation	N
Predicted Value	3.2213	4.9244	4.2200	.48801	150
Residual	-2.37587	1.41167	.00000	.66684	150
Std. Predicted Value	-2.046	1.443	.000	1.000	150
Std. Residual	-3.527	2.096	.000	.990	150

a. Dependent Variable: DV

Charts

CORRELATIONS

/VARIABLES=PP PR PU DV

/PRINT=TWOTAIL NOSIG

/MISSING=PAIRWISE.

Correlations

Correlations
		PP	PR	PU	DV
PP	Pearson Correlation	1	-.015	-.003	.030
	Sig. (2-tailed)		.856	.973	.720
	N	150	150	150	150
PR	Pearson Correlation	-.015	1	.222^**	.302^**
	Sig. (2-tailed)	.856		.006	.000
	N	150	150	150	150
PU	Pearson Correlation	-.003	.222^**	1	.561^**
	Sig. (2-tailed)	.973	.006		.000
	N	150	150	150	150
DV	Pearson Correlation	.030	.302^**	.561^**	1
	Sig. (2-tailed)	.720	.000	.000
	N	150	150	150	150

**. Correlation is significant at the 0.01 level (2-tailed).

RELIABILITY

/VARIABLES=PP1 PP2 PP3 PP4

/SCALE(‘ALL VARIABLES’) ALL

/MODEL=ALPHA.

Reliability

Scale: ALL VARIABLES

Case Processing Summary
		N	%
Cases	Valid	150	100.0
	Excluded^a	0	.0
	Total	150	100.0

a. Listwise deletion based on all variables in the procedure.

Reliability Statistics
Cronbach’s Alpha	N of Items
.766	4

RELIABILITY

/VARIABLES=PR1 PR2 PR3 PR4

/SCALE(‘ALL VARIABLES’) ALL

/MODEL=ALPHA.

Reliability

Scale: ALL VARIABLES

Case Processing Summary
		N	%
Cases	Valid	150	100.0
	Excluded^a	0	.0
	Total	150	100.0

a. Listwise deletion based on all variables in the procedure.

Reliability Statistics
Cronbach’s Alpha	N of Items
.881	4

RELIABILITY

/VARIABLES=PU1 PU2 PU3 PU4

/SCALE(‘ALL VARIABLES’) ALL

/MODEL=ALPHA.

Reliability

Scale: ALL VARIABLES

Case Processing Summary
		N	%
Cases	Valid	150	100.0
	Excluded^a	0	.0
	Total	150	100.0

a. Listwise deletion based on all variables in the procedure.

Reliability Statistics
Cronbach’s Alpha	N of Items
.874	4

RELIABILITY

/VARIABLES=DV1 DV2 DV3 DV4

/SCALE(‘ALL VARIABLES’) ALL

/MODEL=ALPHA.

Reliability

Scale: ALL VARIABLES

Case Processing Summary
		N	%
Cases	Valid	149	99.3
	Excluded^a	1	.7
	Total	150	100.0

a. Listwise deletion based on all variables in the procedure.

Reliability Statistics
Cronbach’s Alpha	N of Items
.880	4

How to place an order:

How to place an order:

How to place an order:

How to place an order:

How to place an order:

How to place an order:

How to place an order:

How to place an order:

How to place an order:

How to place an order:

How to place an order:

It’s so easy to contact us

How to place an order

How to place an order:

How to place an order:

How to place an order:

How to place an order:

How to place an order:

How to place an order:

How to place an order:

How to place an order: