Singaporean Consumer Trust and Loyalty in E-Retailing

Subject: E-Commerce
Pages: 36
Words: 8850
Reading time:
35 min
Study level: Undergraduate


This paper is about the trust and loyalty relationships in e-tailing. It is an attempt to validate the hypothesis on the impact of trust and loyalty to e-tailing. The introduction revolves around the importance of the study with respect to the lack of existing prior studies into the value of trust in an e-tailing environment. The literature review will cover important decision-making criteria and the most recent literature on trust and loyalty as factors in e-commerce. The methodology, results, and analysis follow.

The paper covers theories and assumptions on how trust affects the relationship between e-tailers and customers. In order to test these assumptions, a study was undertaken, and the results debunk or support the hypothesis. Also described therein is the process by which the survey was undertaken and the reasons as to why the survey was done the way it was. The sound statistical theory supports the sampling method.

Certain aspects of trust were debunked, while others were affirmed. Analysis showed that as increased modernization calls for many transactions to be made possible online, trust in security has become paramount. The limited amount of literature directly addressing trust and loyalty provides for broad-range of the hypothesis that can be formulated. Application of this learning is also included in how it can contribute to making e-tailing businesses more competitive

Introduction and Objectives


Shopping for goods and services over the Internet and making purchases has become virtually a worldwide phenomenon, at least in economies sufficiently advanced to boast connections to the World Wide Web for a substantial segment of the population. Electronic commerce, more commonly “e-commerce,” “eCommerce,” or “e-tailing,” is the generic term for facilitating the purchase of products or services over the Internet and mobile telephony networks.

A review of the literature shows that the volume of e-commerce has grown extraordinarily since the advent of Internet Service Providers (ISPs) gave the residential market a pathway to the Web. On the part of merchants, putting up a Web presence has been stimulated by the reduction afforded in supply chain and overhead costs of “brick and mortar” stores.

On the part of consumers, the convenience of shopping from home was augmented by the growth of electronic fund transfer methods., exposure to various forms of Internet marketing, secure online transaction processing, E-bay and Amazon, two survivors of the dot-com bust of 2001, are by-now classic examples of sustainability based on serving a need that consumers were willing to pay for.

The relationship between e-tailing and the Web comes from the fact that e-tailing is, with few exceptions, done completely electronically. For example, money is paid for access to premium content such as pay-per-view webcasts or to view the Olympics live from the comfort of one’s home before the TV networks catch up with their delayed-for-advertisers broadcasts.

The entertainment value of the Web also fashioned the second type of business model. In the case of massively-multiplayer-online-role-playing-games (MMORPG), it turned out there is a class of players that is willing to pay hard currency for access or for premium items in an otherwise free-access game.

However, a vast majority of e-tailing involves actual physical goods and services sold through the Web and delivered with the efficiency of the worldwide package courier companies. The Internet serves as a convenient shopping medium and thereby

replaces malls or physical stores where the goods are on display. Instead, the consumer chooses goods or services from the convenience of home or office.

Three broad segments make up the market. The first category involves online reservations and purchases for tickets or hotel accommodations, usually for business or travel for pleasure. The second category is the more conventional purchase of products which will then be delivered to the buyer’s home. Thirdly, a burgeoning segment of the e-commerce market is devoted to offering services in the guise of paying for downloadable PC software, movies, or songs.

The latest estimate is that Singapore already had 99% connectivity or nearly one line for every citizen of the Republic. This is unmatched by other countries though one concedes the count includes government and business facilities never used for personal surfing or e-commerce. In 2008 the three-month spending average of Singaporeans reached a whopping US$770.7, 25% greater than the Southeast Asian regional average of US$612.40. Just how fast spending has grown is indicated by Visa Cardholder spending that stood at just S$55 million in Q2 2006 (Hardwarezone, 2008), up 45% increase on a year-ago basis. While not strictly comparable, the 2005 and 2008 figures do suggest the great leaps that e-commerce expenditures have made lately.

Statement of Purpose

This study updates contemporary research by examining the antecedents of consumer trust in e-tailing using the dyad of trustor – i.e., the consumer and trustee – i.e., e-commerce vendors and their respective risk perceptions. Subsequently, the study focuses on consumer attitudes towards vendor and their risk perception, which affects willingness to buy.


The study analyzes the aspects of consumer trust. The author posits that understanding the situation means breaking down the trust paradigm into how the consumer’s innate characteristics and perceptions of a vendor’s characteristics combine to affect trust in the vendor. This trust, in turn, qualifies the vendor for inclusion in the consumer’s choice set, leaving the possibility of a purchase and repeat patronage open for the future.

H0, the null hypothesis, posits that there is no relationship between vendor characteristics and the consumer attitudes or beliefs that result in trust.

H1, the alternative hypothesis, states that vendor characteristics such as reputation and size, multi-channel integration, and system assurance have a significant relationship with, and are therefore predictors of, the consumer attitudes and perceptions that redound to consumer trust and loyalty.

Importance of Study

Given the proclivity of Singaporeans for online purchases, as illustrated by comparatively high per-capita outlays, it stands to reason that:

E-tailers already doing brisk business can shift their strategic emphasis from building trust to enhancing their average “share of pocketbook.”

Start-up or marginalized e-tailers and foreign-based sites wanting to enhance their market penetration can take a cue from the important predictors of trust to learn how to position themselves more optimally and do likewise for their Internet marketing messages.

For both vendor types, it is an irreducible truth that, in an increasingly competitive environment, even slight but perceptible changes in perceived trust can spell the difference between survival and bankruptcy.

Literature Review

Introduction of Literature Review

The literature review’s first section will cover a critical review of the major literature on the decision-making process models. The next part will cover available literature on what is known as the communications mix of e-retailers, also known as the 7Cs.

The third part covers the impact and development of e-retailing in recent years. This literature topics list is of interest. The writer aims to establish a link between improved attention to the 7Cs and the rapid growth of the e-tailing business.

Consumer Decision Making Process Models – Literature Review

Richarme (2005) relates how early economists, such as Nicholas Bernoulli, Oskar Morgenstern and John von Neumann, puzzled over the question of how a consumer arrives at his or her buying decisions. Starting around 300 years ago, Nicholas Bernoulli developed one of the first formal explanations for consumer decision making. This explanation was later expounded upon by Von Neumann and Morgenstern and was dubbed the Utility Theory.

Herbert Simon, a Nobel Prize Laureate, proposed his simpler alternative model in the mid-1950s. He called his model “Satisficing”. Satisficing is the term he used to imply a process by which consumers get approximately where they want to receive and then stop in their decision-making processes. In other words, they satisfy their want and then stop right there.

The Prospect Theory was developed in the late 1970s by two of the leading psychologists of that time. They were Daniel Daniel Kahneman and Amos Tversky. Their theory built upon the points raised in the Utility Theory and Satisficing Theory and solved many problems that the first two theories presented.

On the other hand, Ivan Petrovich Pavlov, a Russian psychologist in the early 1890s, first described the phenomenon now known as classical conditioning in his experiments with dogs and meat. In his original experiments, Dr Pavlov rang a bell then gave meat to a dog which salivated at the sight of the meat. After performing this conditioning enough times, the dog would salivate even without the meat so long as it hearts the bell. Need or motive, Attribute or Stimulus, Response or Behavior, and Reinforcement are the four elements of the Pavlovian model. Blackwell, Miniard and Engel (2001) have credited Pavlov as the father of classical conditioning. Classical conditioning is now a term associated with Ivan Pavlov and is still widely used in the context of consumer behaviour today.

Other major works in consumer behaviour involving psychology, economics, sociology and anthropology were developed by Nicosia (1966), Howard and Sheth (1969), and Engel, Kollat and Blackwell (1973). Marketers sought psychological explanations to better understand the process by which consumers make a purchase decision.

Psychology has been a core discipline in marketing because it yields insights into the cognitive and emotional processes underlying human behaviour. Whether a purchase decision is chiefly based on rational or affective considerations, Statt (1997) asserts, it involves the existence of multiple possibilities to choose from, and they all bring into play the same psychological process of giving value in return for receiving a benefit or tangible value.

Buyer Decision Making – The Nicosia Model

One of the first to pioneer findings and theories in the behavioural sciences is Frank Nicosia (1996). His work centred around the formation of three buyer decision making models. He called his models the Univariate model, the multi-variate model and the system of equations model. The most comprehensive of these involved five modules: The encoding module bares such determinants “attributes of the brand”, “environmental factors”, “consumer’s attributes”, “attributes of the organisation”, and “attributes of the message”. The other modules in the system are consumer decoding, search and evaluation, decision and consumption. A simpler version of the Nicosia Model was advocated Al-Jeriasy (2003) since the Nicosia Model appeared to him as an exercise of comparing inputs of a comprehensive model that was largely based on a consumer’s analysis of the content of an enterprise message which was then correlated what a consumer value in a product.

Unfortunately, his model is subject to certain limitations, as seen below:

Firstly, an assumption is made that the enterprise message sent will be about a product or service which the consumer is actually unfamiliar with and therefore, he compares the message’s properties with his own needs. As a result, he will then adopt a belief towards the product or service and sees if his beliefs on it meet his actual requirements. This presumption of moving from outright ignorance to a working full product awareness is the principal weakness of the theory. However, like many other shopping decisions, the customer usually already has a prior judgment.

Secondly, the model is limited because it does not provoke any applied studies to actually test or validate this hypothesis in a practical manner.

The Buying Decision Process – The Five-Stage Model

It was suggested by Kotler and Keller (2003) that it is undergoing the buying process. The consumer will undergo a “stage model”. According to them, there are five stages in their model. These are; problem recognition, information search, evaluation of alternatives, purchase decision, and post-purchase behaviour. This is typical for, say, home cleaning equipment or for replacing worn-out shoes. But it also applies to dissatisfaction with one’s facial complexion, desiring Caucasian-type, paler skin or beating out the competition at the school prom.

It was Philpps and Simmons (2002) who urged that the five-stage decision process models are actually derived from the general decision-making / problem-solving models of researchers such as Newell and Simon.

The Howard-Sheth Theory

Howard and Sheth (1969) presented a theory that listed seven instances of integration in the customer buying process. It remains one of the most comprehensive consumer behaviour models. Not only does it describes applicable motives, but it also deals with purchase decision-making phases and elements.

Individual Customer Decision Making and the Three Customer Roles

Much later, one of the co-authors worked with another to condense the steps consumers undergo into five (Sheth and Mittal, 2004). What remained were the essential steps of problem recognition, information search, evaluation of alternatives, purchase and post-purchase.

This new conceptual framework is valuable because it deals with the three different roles that a customer takes that of a payer, user and buyer, where buying and consumption take place and the cost. The model also brings up the concept of “mental budgeting”, which is actually distinct from the payer role.

Consumer Decision Process Model – EKB vesus CDP model

Professors Engel, Kollat and Blackwell at the Ohio state university developed the CDP model, and thus, their model was known as the Engel-Kollat-Blackwell or “EKB” model. Their seminal work in 1968 was the development of a working research document on the relationship of behavioural sciences to marketing (Baker, 2001). Engel, Blackwell and Kollat (1978) used five steps (problem recognition, search, alternative evaluation, choice and outcomes) as the basis for their model.

Later Professor Paul Miniard joined them as the co-author of this text and thus was renamed the EBM model Engel, Kollat and Blackwell (1995). The EKB model refers to the pre-purchase stage and its three elemental stages: first of all, there is Problem Recognition, second, there is information search, and third, there is evaluation of the possible alternatives. After these three stages are done, there is a post-purchase stage which also deals with evaluation.

Blackwell, Miniard and Engel (2001) thus defined a Consumer Decision Process model that represents a roadmap of consumers’ minds that marketers and managers can use to help guide product mix, communications and sales strategies.

According to the CDP model, a customer will typically undergo seven distinct stages when making a decision: (1) need recognition, (2) search for information, (3) pre-purchase evaluation, (4) purchase, (5), consumption, (6) post-consumption evaluation, and (7) divestment.

Baker (2001) urged that, although the literature reviewed by Engel et al. is more thorough than Nicosia’s, their model is less detailed and has not stood the test of mathematical or simulation tests. One criticism is that, as with Nicosia, the search and evaluation process is portrayed as being highly rational as opposed to being highly emotional.

There were, in fact, some similarities in the work of Engel et al., Nicosia and Howard and Sheth. They both incorporated the concept of post evaluation, feedback or customer satisfaction. All three are core concerns of Customer Relations Management as marketers practice it today.

It has been suggested by Gary (2001) that the stages of decision-making are actually opportunities for the selling to form and create a longer-term relationship with its customers. Gary also adopted Howard’s previous work (1963) with respect to his exploration of the dynamics of the buying process among being (a) extensive (b) limited (c) routinised problem-solving.

Herbert Krugman, according to Mowen and Minor (2000), was the first person to postulate that the decision process is distinct between high-and-low involvement purchases. Krugman suggested that the decision-making process is extended when there are high-involvement conditions. On the other hand, limited decision making and less search behaviour are the rules in low-involvement conditions. An example of the former for a male shopper would be buying a new PC or car, while toilet rolls would typify the low-involvement, “generic” purchase.

Hollensen (2003) agreed that the first 3 of the five steps in the consumer process could be dispensed with in low involvement purchases. As involvement increases, each step takes greater importance, and more active learning occurs. Active learning occurs coincidentally to the exploration of the vast spectrum of alternatives available of the Intenet.

Need Recognition (CDP – stage 1)

The principal step of the EKB model suggests that problem recognition begins when there is a significant variance between a desired or ideal state and the actual state in relation to a particular need. Need recognition occurs when an individual senses a difference between what he or she perceives to be ideal versus the actual state of affairs. Need recognition is linked to memory, environmental influences (culture, social class, personal influences, family and situation) and individual differences (consumer resources, motivation, knowledge, attitudes, personality, values and lifestyle).

Information Search (CPD – Stage 2)

According to the EKB’s model, an internal search is mainly concerned with the person’s memory of previous experiences with a similar specie of purchase. External search can include information from both personal sources, e.g., family friends, experts, and impersonal sources such as advertising. Information search may be internal, retrieving knowledge from memory or perhaps inbred impulses, or it may be external, collecting information from peers, family and the marketplace.

Pre-purchase Evaluation of Alternatives (CDP – stage 3)

Alternative evaluation is the third step in the EKB model. The four major components that will affect the evaluation of alternatives are the key inputs at this stage. These key inputs are. (1) evaluative criteria; (2) beliefs; (3) attitudes; and (4) intentions.

Evaluative criteria are the chief concern of the pre-purchase evaluation stage. Past experiences are the primary influence on Evaluative criteria. Such influences include new or pre-existing valuations stored in memory, environmental influences such as culture, social class, personal influences, family situation and individual differences such as consumer resources, motivation, knowledge, attitudes, personality, values and lifestyle.

Purchase (CDP – Stage 4)

In the course of business, the customer may actually purchase something that is different from what they planned to purchase or may even not buy anything at all because of what occurs during the purchase choice stage.

Consumption (CDP – Stage 5)

Like the above, this stage is also not defined in the EKB model. Consumption can either occur immediately or be delayed. Sales promotions, when purchased their goods without actually having a need or simply desiring an object because it was well promoted to him or she is otherwise known as a consumption delay.

Post Purchase Evaluation (CDP – Stage 6)

The EKB model suggests that once the pre-purchase stages are completed, the final stage, which is post-purchase assessment, occurs. This terminal stage of the decision process is singularly concerned with the satisfaction the consumer feels and the disposition of the product. A consumer can experience either a sense of satisfaction or, in the alternative, dissatisfaction.

Consumers experience a sense of either satisfaction or dissatisfaction. The outcomes are significant because consumers store their evaluations in memory and refer to these evaluations in their future decisions.

Divestment (CDP – Stage 7)

At this stage, the consumer has several options. He may opt to dispose of an object outright, recycle, or remarket it.

EKB vs. CDP Model

The CDP model has three additional stages: Consumption, Divestment and Purchase, which are not found in the EKB model. The additional stages, in theory, will provide a more detailed understanding of the consumer decision process. However, it may prove difficult to provide analysis for these stages as they are not as measurable as the previous stages.

E-retailing Marketing Mix 7Cs – Literature Review

The e-retail business has been defined as the sale of goods and services via the Internet or other electronic channels for personal or household use by consumers (Harris and Dennis, 2002). Whereas McCarthy (1960) developed the original marketing mix of “4Ps” (Place, Product, Price and Promotion), Chaffey et al. (2003) opined that shopping on the Internet is more likely to require a marketing mix addressing seven Ps in all (Production, Promotion, Price, Place, People, Process, and Physical Evidence, [Blooms and Bitner, 1981]).

The “People” factor is essentially about market segments. In the local context, religion and offering “halal” products loom large only when a Singaporean food processor aims for a fair share of the cross-isthmus Malaysian market too. And given the turbulence imposed by oil-producing countries and their surrogates, the global “Seven sisters”, changing consumer motivations need to be plumbed to see if an entire e-commerce site can be organised around fuel-saving devices or whether car-owners would rather shift completely electric and ethanol power trains.

It was emphasised by Lauterborn (1990) that a consumer’s wants and concerns are modelled in his “4C’s” model, which is composed of Convenience for the customer, Customer Value and benefits, Cost and Communication. It was urged by Chaffey et al. (2000) that an alternative “6Cs” be presented as the key to success factors for an e-tailing website: Capture, Content, Community, Commerce, Customer Orientation and Credibility.

Allen, Kania and Yaeckel (2001) suggested that the data analysis of raw data about Web site visitors would provide useful information on consumers’ wants and needs. This is commonly referred to as “data analytics”, with personal information elicited on the promise of a “freebie” or additional privileges.

To be taken seriously and trusted, Whiteley (2000) argued that a full-service e-store needs an extensive range of facilities, including posted company information customer registration. Dynamic (visually pleasing) Web pages, site indexes, online order and payment facilities, sophisticated transaction security systems, after-sales service, and support and feedback channels.

The E-retail mix – 7Cs

By way of responding to the trust imperative, Dennis, Fenech and Merrilees (2004) suggested an E-retail mix of 7C’s as opposed to the earlier 4Cs and 6Cs:

Convenience – Navigability

Physical location, multi-channel options, Virtual location and ease of finding the website, website design and layout.

If the homepage layout is easy to navigate, this allows the information to be logically organised, clues in the user’s location and reminds him/her which sections of the website have already been visited (Kalyanam & McIntrye, 2002). On the other hand, Schaffer (2000) argued that a convenient website provides a short response time, facilitates fast completion of a transaction, and minimises customer effort: Therefore, a free grid layout enables the user to find information according to its category and to access the desired product more easily.

Customer Value and Benefits

All buyers desire the satisfaction of wants, and this includes solutions to problems, engendering goodwill,

or good feelings and a range of products to meet the wants of the targeted customer segment as closely as possible.

Given the convenience paradigm that looms large in e-commerce, reduction in time required to make online purchases (as opposed to travelling to a store and the convenience this entails) is the principal value that e-tailing can provide to consumers. The value of time as a major variable of interest to consumer behaviour theory has already been recognised even in the earliest stages of consumer research (Nicosia 1966, Howard and Sheth 1969, and Engel et al. 1973). Customer satisfaction drives loyalty, and loyalty, in turn, drives profitability and revenue growth. Reichheld and Sasser (1990) suggested that a 5% improvement in customer retention can yield an increase in profitability between 25% and 85%. This happens because repeat customers represent zero marketing cost.

Cost to the Customer

This refers to the real costs customers ultimately pay, including transportation, carriage, taxes. And the costs of Internet access. Customers tend to uphold the perception that prices should be lower online than in a physical store.

Time, effort and emotional investment are just a few examples of the non-tangible and intangible costs incurred by the consumers. Aside from these non-monetary costs, there are also actual monetary costs. (Dholakia & Uusitalo, 2002). While a customer may have to pay for internet access, travel costs are dispensed with along with the time wasted going to the actual physical store Richard Yamarone, an economist at Argus Research Corp, postulated that high gasoline prices, which show no sign of abating, gives consumers a good reason to refrain from shopping in the malls and doing their shopping online instead. Rather than visit the department store showrooms, consumers can make online comparisons and reduce time spent in the selection process.

Communications and Customer Relationships

Communication is a two-way process involving feedback from customers to suppliers and the supplier offering products to the customer. The standard tool for gathering such information is research surveys, while communication from the seller to the market employs direct mail, traditional (offline) advertising, online methods and broadcast media.

Associate Professor Thomas Tan, who specialises in retailing at Singapore Management University, urged the importance of the “responsiveness” element in a website. He emphasised that it should be easy for customers to communicate with the Web store. For this, tools like “Contact Us” fields, Help sections or Feedback Forms are vital (Straits Times, 2005). Strauss (2003) mentioned that while e-commerce transactions are an exciting dimension of an e-business presence, other objectives are also worthwhile, especially when the firm is using technology mainly to create internal efficiencies in target market communication.

Person-to-person contacts on the Internet that are transmitted and received instantly or with a lag of only a few seconds are also known as Real-time communications (Dann and Dann, 2004). In the modern era, there are many iterations of this technology, such as Yahoo Messenger, Skype and Meebo. “Stickiness” is a term that refers to the process of getting people who browse the web to spend more time on a given website (Tiernan, 2000).

According to Monsuwe et al. (2004), if a personal interaction with a salesperson or pre-trial is necessary for the product under consideration to be purchased, consumers’ intention to shop on the Internet is low, as is the case when one is buying a car.

Computing and Category Management Issues

Category management bespeaks the discipline of supplying the products that customers want, in the right sizes, quality, colour and style range, in a timely and in the right place.

This set of tasks requires, among others:

An efficient supply chain with computer network links (also called EDI) between suppliers and retailers.

Balancing minimum stock-keeping while maximising availability is Efficient Customer Response (ECR – the retailers’ equivalent of Just-in-Time or JIT in manufacturing).

Efficient logistics systems – an important component of customer care and service

Customer Franchise

Building a compelling brand image in a world where many categories are crowded with “me too” rivals means:

Marketing communications that support image, trust and branding, as well as a degree of investment in customer care and service. This is so because Brand Familiarity positively influences intention to buy the brand through the high level of confidence in the brand (Laroche, 1996).

Maintaining safeguards, including those against fraud protection (e.g. VeriSign) and dispute resolution.

Safe shopping icons

The security of the website and the company’s systems and the security and privacy of individuals who interact with the company (Tiernan, 2000) were a huge concern at the dawn of the e-commerce age.

Customers evaluate their Internet shopping experience based on perceptions of product information, a form of payment, delivery terms, services offered, privacy, security, personalisation, visual appeal, navigation, entertainment and enjoyment (Burke, 2002).

Customer Care and Service

Creating assortments at competitive prices in an accessible format.

Fast and reliable deliveries at times convenient to the shopper.

Availability of help; return and refund facilities.

Addressing customer concerns, particularly for credit card security, e.g. Displaying the ‘padlock’ secure site logo.

Good customer service is critical to building long-term relationships. Uncles (1994) espouses the view that customers actively seek an involving relationship with ‘their’ brand. It is important to be able to identify the stage in the decision-making process consumers is at in order to best satisfy their individual needs.

In establishing an online presence as an adjunct to their existing physical stores, retailers encounter the difficulty of not being able to use the same format for both online and traditional stores (Monsuwe et al., 2004). Modahl (2000) asserts that great marketing companies respond rapidly and encouragingly to emails originating from the website and coordinate telephone and email services

Trust and Loyalty Paradigms in Practice

Singapore has an estimated population of 4,588,600 as of 2007, yet as of February 2008, there were already 4,540,400 Internet subscribers, a near one is to 1 ratio. This suggests that virtually all people in Singapore have access to the Internet on a daily basis. The government has been promoting the use of broadband Internet access as part of its Intelligent Nation 2015 plan. Singapore, a small, densely populated island nation, continues to be one of the few countries in the world in which broadband Internet access is readily available to just about any would-be user anywhere in the country, with connectivity rates exceeding 99%. However, there are two major obstacles to successful e-tailing. First is the inherent lack of trust in Internet commerce brought about by an absence of human contact. Second is the difficulty of gaining customer loyalty in an e-tailing setting.

E-tailing and Singapore’s Internet connectivity

Singapore also has one of the highest levels of online spending in Asia (Asia Travel Tips 2008). The three-month spending average is a whopping US$ 770.7 compared to a regional average of US$612.40.

Internet Penetration Rate, Income Level and Culture: key driving factors for online shopping Penetration

There is an 87.1% chance of a strong relationship between online shopping penetration and these key factors. Areas with high Internet penetration rates consequently have higher shopping penetration rates. Countries with low Internet penetration rates have proportionately lower online shopping penetration rates. Cultural affinity for online shopping also appears to be a factor because some socio-cultural groups are averse to e-retailing.

Shoppers conduct research and plan their online spending

Based on a recent survey, 84% of online shoppers do their shopping plans in advance and conduct research before making a purchase (Asia Travel Tips 2008). Methods employed include browsing the web as a whole, lurking in the merchant’s website, and word of mouth. Opportunistic shopping is not a factor for many online shoppers, with only 10% of respondents from Singapore saying they bought items on impulse. These purchases are usually conceded due to low prices, unique online-only items or in response to persuasive advertising and promotions.

Trust in E-tailing

Credit cards are the dominant and preferred mode of payment for online shopping. Yet credit card fraud is one of the most easily perpetrated frauds in the world. MOTO, mail order/telephone order and Internet credit card use is especially at risk since there is no actual credit card swipe, and often all the merchant requires is the credit card number and name of the cardholder. A potential fraudster need only have seen the card in order to perpetrate fraud. Worse, some websites pose as legitimate websites, while legitimate e-tailing sites are not above the temptation to use credit card information for fraudulent use later on.

Recent developments have helped allay fears in online shopping. Internet purchases are now protected with security features such as requiring the CVV number printed on the back of the card or other security information before a purchase is allowed to go through. Many credit card providers like Chase Morgan and HSBC now employ sophisticated anti-fraud tracking departments that seek to minimise illegitimate use of credit cards. Chase Morgan is known for its zero-liability program for all purchases made on a card after it has been reported lost or stolen. These and other measures have helped increase trust in e-tailing.

Logically enough, confidence in Internet transactions has improved. Nonetheless,74% of online shoppers still believe that payments and security systems could be better.

Loyalty in E-tailing

Loyalty suggests repeat purchases. However, the impersonal nature of online transactions hinders creating customer loyalty. A regular customer of an online store can switch loyalties with ease as opposed to one who regularly frequents a shop. Customer loyalty is harder to maintain as the consumer has greater access to information, and he can easily search for a new service provider that meets his needs better. Loyalty is also lacking because there is little to no relationship between the merchant and the customer. In response to this, many online companies resort to emails and other reminders to establish connections with their past customers in hopes of gaining some measure of loyalty.


This chapter has provided the literature review of the existing decision making process models. It has also touched upon literature on the relationship between the E-retailing 7C’s mix and the impact of consumer E-tailing paradigms of loyalty and trust on the decision-making behaviours of potential customers. Based on the literature reviews, the writer has chosen the relationship between e-retailing mix 7Cs and its effect on consumer loyalty and trust for this research study. Specifically, how trust and loyalty for e-tailing outlets have been enhanced or detracted upon by the march of technology. It will also cover how increased attention to the 7C’s can be the key to increased competitiveness. The following chapter shall cover the researchable questions, research methods and techniques.

Research and Methodology

Scope of research / Research Hypotheses

  • The scope of research aims to test the veracity of the following theories on trust;
  • There is a positive relationship between trust and the reputation of a vendor.
  • The larger the vendor, the more likely it is to be trusted.
  • The level of multi-channel integration of an e-retailer has a positive effect on the customer’s perception of its trustworthiness.
  • A system of assurances of an e-tailer has a positive impact on trust.
  • Previous satisfactory purchases have a positive impact on repeat purchases.
  • Positive trust ratings increase a customer’s willingness to purchase from the vendor.
  • Consumer trust towards an e-tailer is negatively affected by perceived risks in dealing with it.
  • Consumers’ negative views on e-tailing are dispelled by favourable experiences in purchasing from one.
  • The perception of risk is dissipated when a customer is willing to buy from an e-tailer.

Method of collection

The survey was undertaken personally by the author in the Central Business District. Various constructs were developed to fit the existing hypothesis of the research. A list of questions was presented before the respondent, and they were asked to rate the items on a seven-point scale ranging from (1) I Completely unimportant to (7) Very Important. The survey was taken in the form of a short questionnaire which the author asked prospective respondents.

The original plan was to do an online survey. However, given the vagaries of the Internet, it was determined that it was impractical. First, convincing people to take the survey would prove difficult considering that people who actually will take the survey seriously. Second, there is little trust in an online survey, some questions asked are of sensitive nature, and the respondent might not be willing to answer them. Third, an online survey could be the target of web vandals.

Instead, person to person survey was taken. Not only would this address the issue of trust, as seeing a live person asking the questions is more conducive to the truth than an online survey, it would also serve to address the limitations on time and resources. A final problem with the online format was that there was really no time to construct the site properly. In a field survey, it was a simple matter to print copies of the questionnaire and go around town to ask people. The desired sample size of 500 was originally intended.

In order to facilitate cooperation in filling out demographical information, the respondents were given small tokens of appreciation.

Structure and design

The first section dealt with questions regarding their online purchasing experiences, types of products or services that they bought in the past., what they would want to purchase in the future. How much they are willing to pay for these desired purchases and the average price, they paid in the past.

The second section required the respondent to choose from three categories as a frame of reference; the three were; books, music, or travel when they answered the questions. The category chosen is ordinarily the product they had purchased in the past. If they had never purchased anything from an e-tailer, then they were asked to answer hypothetically as if they had purchased something in the past. The respondents were asked if they agreed or disagreed about statements regarding specific e-tailers.

The third section contained basic demographic characteristics, including age, gender, nationality, education level, occupation, monthly personal income, Internet experience, etc. Respondents were required to provide a valid email address for the lucky draw. They were also encouraged to fill in any suggestions and comments at the end of the questionnaire.

The questionnaire was presented in Mandarin and English to allow for possible survey takers who did not understand Chinese. The questionnaire, originally written in English, was translated into Chinese by a bilingual person whose native language is Chinese. The Chinese questionnaire was then translated back into English by another bilingual person whose native language is English. These two English versions were then compared, and no item was found to pertain to a specific cultural context in terms of language. This process was conducted not only because it can prevent any distortions in meaning across cultures where necessary but also because it can

enhance the translation equivalence. The questionnaire was pre-tested with bilingual respondents to ensure that the two versions were internally consistent.

Provision for the Chinese language was made in order to assure maximum absorption by a prospective respondent. Although all Singaporeans are capable of speaking English, consideration was given to those who took up English as a second language. As such, a Chinese version was available if the person preferred to take the test in Chinese. Functionally the same, the Chinese version proved to be of little help in the current study other than to facilitate survey taking with those who are less fluent in English. In the future, a larger, more in-depth survey should also have provisions for Bahasa (Indonesian), Tagalog (Filipino) and other languages that people in Singapore who might be surveyed speak. However, this would be far beyond the scope of this survey and more of a looking forward to future work.

Sampling method

Given the limitations on time and financial resources, not to mention the difficulties raised with other methods. Random sampling via face to face interviews was done. The desired sample size of 500 was planned, although time constraints may force a considerably smaller sample size provided that the size does not go below 100 because statistics show that below 100, the formulas for determining accuracy and statistical precision begin to break down. The respondents were randomly selected in the Central Business District of Singapore by the author.

Questionnaire Structure


Name: __________________________________________

Section 1

  • Q1. How long have you been using the Internet?
  • Q2. Do you use the Internet for online shopping
  1. Yes
  2. No

Section 2

Think of the last time you made a purchase at an online store. If you have never bought anything online, just assume that you are making a purchase rate the following statements about trust, from (1) being completely unimportant to (7) very important.

  • Q3. The merchant must be a reputable online vendor.
  • Q4 The vendor must be a large company.
  • Q5 The vendor must have both online stores and physical stores.
  • Q6 The vendor must be reliable, safe and secure.
  • Q7 The e-tailer must be trustworthy.

Section 3

  • Q8 Age
  • Q9 Gender
  • Q10 Nationality
  • Q11 Highest Educational attainment
  • Q12 Occupation
  • Q13 Monthly income

Sources of Bias

The main source of bias is the survey taker himself. Since it is up to his discretion who to ask certain types of people may have been overlooked. Plans for a targeted demographic failed as time constraints began to


A face to face survey was done. Guerilla methods of offering a reward to the respondents were used to attract survey takers. Questions asked were direct responses to the questions raised in this paper while not so biased as to possibly affect the respondents’ decision criteria. Finally, the only likely source of bias is the fact that it was the author who determined how awareness of the survey’s existence was spread, which may create bias.

Findings and Analysis

Sample Profile

The sample comprised 138 valid responses. The gender ratio of the respondents was 52% male and 48% female, which is consistent with Nielsen/Net ratingsi which reported that slightly more than half (58%) of Singaporean internet users were male.

Table 1.

Demographic profile of respondents
# %
Gender Male 72 52.0
Female 66 48.0
Age Under 15 2 1.1
16–20 33 24.1
21–25 65 46.7
26–30 17 12.1
31–35 9 6.5
36–40 6 4.5
41–45 3 1.8
46–50 2 1.7
Above 50 2 1.4
Education level Primary 1 0.4
Secondary 8 5.9
Junior college 66 48.0
Poly/Diploma 15 10.7
Bachelor 36 26.4
Master 9 6.6
PhD 1 1.0
Others 1 1.0
Internet experience (Years) < 4 27 19.6
4to < 7 86 62.2
7to < 10 21 14.9
> = 10 5 3.3
Daily usage of Internet (Hours) < 1 25 18.0
1to < 3 63 45.4
3to < 5 28 20.3
5to < 7 10 7.1
7to< 9 4 3.1
> = 9 9 6.2

In addition, most of the respondents, 94%, were between 16-40 years old, with only 4.9% being above 40. In fact, Internet users are preponderantly older adolescents and young adults (16 to 25 years of age, 71%). Nonetheless, this gender and age distribution is consistent with the findings of the Graphic Visualization and Usability Center’s (GVU) WWW User Survey.ii that the gender ratio is balancing out and that older, presumably more affluent, Singaporeans have become an important segment of users.

For the rest, 11 of 12 Internet users (93%) have completed at least junior college education. Eight in ten have used the Internet for upwards of four years. The typical intensity of usage is 5 hours or less daily (84%).

Variances, means, standard deviations and correlation matrices

Harman’s one-factor test was used to examine the extent of the bias attendant to self-report studies where both dependent and independent variables are collected from the same informant at one point in time (Podsakoff and Organ, 1986); this is referred to as “common method variance.” Since several factors with eigenvalues greater than one were identified and no single factor accounted for almost all the variance, principal component analyses suggest that common method variance is not a concern.

Table 2.

Means, standard deviations and correlations
Measure Mean SD 1 2 3 4 5 6 7 8
PR 5.302 0.990
PS 5.05 1.410 0.488**
IIC 3.858 1.250 0.130** 0.010
SA 5.327 1.174 0.629** 0.341** 0.167**
PTT 4.431 1.055 0.325** 0.208** 0.152** 0.342**
CT 5.167 1.080 0.669** 0.351** 0.178** 0.769** 0.425**
ATTD 5.146 1.215 0.521** 0.252** 0.137** 0.653** 0.329** 0.720**
WTB 4.897 1.370 0.437** 0.093** 0.116** 0.565** 0.249** 0.591** 0.723**
PRISK 2.988 1.297 −0.288** −0.052 0.046 −0.383** −0.105** −0.372** −0.396** −0.403**
∗∗Correlation is significant at the 0.01 level (2-tailed).
∗Correlation is significant at the 0.05 level (2-tailed).

Table 2 depicts the means, standard deviations, and bivariate correlation coefficients of the study factors. Judge et al. (1982) maintain that multi-collinearity need is a serious concern only when correlation coefficients between any two regressors exceeds 0.8 or 0.9. None of the values breached the critical levels (see Table 3 below).

Table 3.

Collinearity statistics
Tolerance VIF
PR 0.511 1.958
PS 0.754 1.327
IIC 0.957 1.045
SA 0.577 1.733
PTT 0.852 1.174

Multi-collinearity was also tested with the Variance Inflation Factor (VIF). Being the reciprocal of tolerance value, a larger VIF raises the regression coefficient variance and indicates the estimate is less and less stable. The larger the VIF value and the more it exceeds the recommended cut-off of 10, the greater the degree of collinearity or multicollinearity among the independent variables. That none of the computed VIF values even breached a value of 2.0 suggests that there is no multicollinearity among the IVs.

Hypothesis Testing

Figure 1 (below) demonstrates that three of the five hypotheses regarding predictors and antecedents of buyer trust with respect to eCommerce sites are borne out.

There are significant relationships between perceived reputation (H1: μ =0.568, p<0.01), system assurance (H4: μ =0.550, p<0.01), and propensity to trust (H5: μ = 0.115, p<0.01 ) on the one hand, and consumer trust in e-commerce vendors, on the other hand. The high μ’s for both H1 and H4 suggest that both the “corporate image” or familiarity of the site and the system assurance of an e-commerce vendor have a major influence on building trust and, therefore, propensity to patronize the site.

In contrast, the perception consumers have about the size of the e-commerce operator (H2: μ = 0.012, p>0.05) shows no positive relationship with consumer trust, suggesting that e-tailer patrons have matured to the point where they are aware they can get reliable service and quality products even from micro-and small-scale entrepreneurs. This is the reality of the Internet today. Another service feature that does not necessarily engender buyer trust, multi-channel integration (H3: μ = 0.022, p>0.05), may well be explained by the fact that the convenience of quick feedback does not necessarily rank high in B2C (business-to-consumer) priorities. It is more likely the B2B customer who is time-conscious and appreciates the choice of unified communications options such as Internet chat or toll-free lines in order to facilitate receipt of an order.

From the reverse direction, trust has a statistically significant and positive relationship with attitude toward the vendor (H6: μ =0.807, p<0.01). In turn, attitude impacts favourably on willingness to patronize the vendor (H7: μ =0.656, p<0.01).

On the negative side, any perception of risk impacts adversely on the propensity to trust an e-commerce site (H8: μ =−0.502, p<0.01). Risk perceptions also degrade attitudes towards e-commerce site operators H9: μ =−0.103, p<0.01). All in all, any tangible risk is inversely related to willingness to buy (H10: μ =−0.150, p<0.01).

Structural equation modelling (SEM)

Table 4.

Construct reliability analysis.
Construct No. of items Composite reliability Variance extracted
PR 4 0.89 0.67
PS 3 0.84 0.65
MI 2 0.77 0.63
SA 4 0.96 0.85
PTT 3 0.94 0.84
CT 4 0.93 0.78
ATTD 4 0.94 0.81
WTB 5 0.94 0.77
PRISK 4 0.91 0.72

AMOS 4 was employed to carry out SEM. First of all, validity was scrutinized by scrutinizing the factor loadings and the squared multiple correlations (SMC, R2 is in linear regression) between the items and the constructs. (Bollen, 1989). After dropping items with SMC lower than 0.4, all others evinced high factor loadings at p<0.01.

Composite reliabilities were also calculated and were higher than 0.7 in every case; at the same time, variance extracted measures exceeded the recommended 0.5 cut-offs (Hair et al., 1998) (see Table 4 on prior page). These all imply that the model constructs are reliable.

For estimating exogenous and endogenous variables, the model fit indices were employed consisted of Goodness-of-Fit Index (GFI), Adjusted Goodness-of-Fit Index (AGFI), Normed Fit Index (NFI), Tucker-Lewis Index (TLI), Comparative Fit Index (CFI), and Root Mean Square Error of Approximation (RMSEA). The resultant GFI is higher than the 0.9 recommended benchmark (see Figure 1 overleaf), AGFI exceeded the 0.8 thresholds, and NFI, TLI, and CFI all emerged above the 0.9 hurdles.

At the same time, the RMSEA was satisfyingly lower than 0.05; this is the model metric for a very close fit. Hence, all the above model fit indices suggest a good fit for the exogenous and endogenous variables.

Structural model.
Figure 1: Structural model. Unstandardized path coefficients appear on arrows and SMCs inside each latent variable’s ellipse. ∗∗p<0.01, ∗p<0.05.

Summary and Conclusions


The results give credibility to the hypothesized model that consumers, in fact, trust e-commerce vendors. Commercial e-tailing vendors’ characteristics of perceived reputation and system assurance congeal well with the customer’s propensity of trust. They are the primary determinants of consumer trust in Online vendors and have similar weights. However, consumer trust is not statistically affected by the perceived size of a firm.

A likely explanation for the lack of an actual positive relationship between size and customer trust is that an e-tailing firm’s perceived size is not as significant to the customer as the size of a traditional firm would affect perceptions. After all, the size of an Online firm is not quite as easy to grasp as the physical size of an actual firm. For example, a multi-story mall’s imposing size is far more tangible than mere numbers and statistics that serve as an abstract idea of the size of an online firm. Therefore, customers may not actually care about the size of the e-tailing firm they are dealing with. Hence, the hypothetical positive relationship between perceived size and trust is debunked.

The consequences of customer trust are consistent with the past finding of Jarvenpaa et al. (2000). There is a positive relationship between consumer trust and their attitude towards the vendor and the resultant willingness to purchase goods from the said seller. When a customer trusts an e-tailing firm, this trust reduces his perception of the risk involved in making transactions online. Risk perception has a negative relationship with a customer’s willingness to purchase items.

As expected, there is a strong negative relationship between trust and risk perception in e-tailing firms. The higher the risk perceived, the lower the trust for the firm. Furthermore, perceived risk also has an impact on the customer’s willingness to buy from the vendors. This inverse relationship between risk perception and willingness to buy is consistent with past studies. As the primary method of transacting business with an e-tailing firm still involves the use of credit cards, customers are naturally averse to divulging their credit card number and security information to firms that they do not trust. If a lack of trust or perception of risk prevents the customer from divulging security information, then they are even less likely to purchase something from the distrusted e-tailer since the last thing a Credit cardholder would want to do is give an illicit company an excuse to change their card.

One conclusion that can be drawn from this is that security and reliability are critical to an e-tailer’s success. They are vital factors in generating trust for the firm and thus vital in securing sales. For consumer’s who rely on online transactions, trust and privacy are of primal importance. Little research has been done establishing the relationship between systems of assurance and consumer trust, especially in the context of online shopping.

An unexpected result of the survey is that multi-channel integration has no statistically significant relationship to consumer trust. Apparently, the primal drive for online purchases has more to do with price to value perceptions than any belief in post-purchase customer support or other benefits of multi-integration. A possible explanation is that in the search for low prices or a similar value proposition, a customer is willing to ignore the flaws that a multi-level integration solves. Due to the focus on other value propositions, the customer and the e-tailer both do not pay much attention to the integration level. To a customer, it is at best a frivolous service that could be done away with in favour of even lower prices.

In sum, all findings point to reputation and branding as being the key factors in ensuring trust in an e-tailing firm. Systems of assurances are also valuable in generating trust for an e-taling firm. Multi-channel integration and perceived size have a little significant effect on trust perceptions.

Implications and conclusions

As raised in the literature review, theory-guided empirical studies on consumer trust in e-tailing remain rare, and as a result, existing knowledge of trust factors in e-tailing is limited. This paper tries to fill that gap by creating empirical data based on the Singaporean context. It is believed that this paper will contribute to trust literature in the years to come.

The results show that the characteristics of the trustees’ names, perceived reputation and systems of assurances of an e-tailing vendor and the tendency of the customer to trust congeal into becoming critical determinants of trust for e-commerce. While it is logical to assume that trust is a two-way street in that a trustee must have aspects that the customer is willing to trust and the customer must be willing to give trust, to begin with, this paper has made empirical data about this relationship.

A unique conclusion drawn from this study is that a system of assurances is the strongest determinant among the characteristics of an e-tailer that generates trust. An online vendor must take measures to assure a would-be customer that his privacy and security is held in high regard. Given the nature of online purchases, a customer is only naturally averse to divulging. Such provisions are perhaps even critical in obtaining an online business at all.

The weak connection of Multi-level integration as an antecedent of trust suggests that an e-tailing vendor should merely focus on the online aspect of his business rather than try to expand into other levels of being a vendor, as this will merely entail costs which the vendor can ill afford. On the other hand, if a business already has a physical store and a telemarketing section, then nothing behoves it from continuing such sections of the business while keeping an online department.


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