PreMaster Assignment

8/12/2019 PreMaster Assignment

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Margareta Citra Kusumadewi

S1176846

20 th August 2012

Supervisor : Guido Ongena


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Summary

The lack of use of e-commerce by Dutch seniors is the topic of this research. Although Dutch

seniors are perceived to be more active on the Internet nowadays, this group still shows less

interest in e-commerce compared to other activities in the Internet. A group called Beweging

3.0 has requested to understand the factors that can influence the seniors intention to use e-

commerce. The desire to understand the factors that influence the use of e-commerce is due to

the fact that the company is interested to create an e-commerce website intended for seniors.

On the basis of a literature review on e-commerce and its adoption, it was found that

technology adoption, social influence and trust are the variables that are discussed most

frequently when it comes to e-commerce.

For this research the survey method was applied and the items were borrowed from the

original items defined by the inventors of the relevant theories. The respondents in the sample

were given a set of questions in the survey in order to measure their acceptance of e-commerce, the social influence variable, and also the institution-based trust variable. In this

research, the users acceptance towards e-commerce was tested by a sample of 125 Dutch

seniors, aged above 55 years old who had experience in using e-commerce.

The collected data from the research was identified the factors that influence the users

attitude and intention to use e-commerce. The Perceived Usefulness and Perceived Ease-of-

Use are found to have a significant relationship with attitude. Perceived Usefulness is strongly

related with Perceived Ease-of-Use. In addition to the Technology Adoption Model variables,

social influence was found to be an important predictor for behavioral intention and for

institution-based trust. Compared to other variables of institution-based trust, situational

normality integrity is the strongest predictors of behavioral intention.

Besides finding the important factors that determine users acceptance of e-commerce, the

risk reduction items were also tested and correlated with the institution-based trust variable. It

was found that a third party logo, protection of privacy policy, the use of a high quality brand,


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and a money back warranty are the most important strategies to reduce the anxiety and also to

increase trust from the Dutch seniors.

Finally, this research has not only an added value to scientific theory, as also

recommendations on how to increase the behavioral intention of seniors in using e-commerce

are provided.


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1 Introduction and Problem Analysis

Technology has a great role in peoples lives nowadays and it changes the way people live.

One the products of technology is the Internet. The Internet has been a useful and popularmedia among people from different age groups. One of the demographic groups is the seniors

or mature group. In The Netherlands, the seniors engage in online activities through the

Internet such as ordering or buying goods or services, filling in important forms, reading or

downloading newspapers, utilizing the services from transport companies, searching for

information, doing internet banking, searching for information around goods and services, and

sending and receiving email (Central Bureau of Statistic (CBS), 2011). In addition, accessing

the Internet has become more flexible than in the past. People are actively choosing the mediafor example computer, mobile phone and tablet pc, to get the most convenient experience in

performing their online activities.

Various companies perceive that the growth of technology including the Internet to be one of

the factors that supports the expansion of the companys business, particularly to be generated

online. Different type of businesses can be found nowadays in the Internet, particularly

business-to-consumers (B2C). According to Thuiswinkel (2011) there are approximately

37,500 online shops in the Netherlands and the number will be growing in the upcoming

years. By performing online shopping, the presence of the customers in the shop is not as

necessary as in the past, since the global access of the Internet allows people all over the

world to be able to shop in the desired time and place. E-commerce offers great benefit for the

companies such as 1) allowing the company to reach a larger customer base and build closer

relationships with them more easily, 2) reducing the operations cost e.g. staff cost (Chaffey,

2004). In addition, the growth of technology has enabled the paying system to be simpler than

it used to be and this has been another added value of online shopping that attract more

customers to shop online.

Nowadays, customers are showing great interest in performing online shopping. For instance

in The Netherlands, the number of people who are interested in performing online shopping is

increasing over time. The central office of statistics from the Netherlands (CBS) shows that

9.3 million people age 12 until 74 years shopped online and in addition, the total numbers of

online shoppers is increasing compared to previous years (CBS, 2011).


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In general, the users of the Internet have different backgrounds and to be more specific, this

also applies to the users of online shopping websites. One imperative background

characteristic is age. The research from CBS shows that the age range of online shoppers in

the Netherlands is a mixture between the young and the old populations. CBS (2011) indicates

that seniors nowadays become more motivated to use digital media and also being active in

the Internet. Six out of 10 people aged 65 until 75 years old in The Netherlands use the

Internet frequently. This number has doubled compared to the rate in 2005. Although the

research conducted by CBS shows that the rate from the seniors being active in the Internet

has increased, the adoption of online shopping websites is still relatively small as only 30%

seniors out of the total population performs online shopping.

The seniors group or alternatively known as the mature group is a group that consists of

people aged above 55 years old. Nevertheless, the seniors itself can be grouped into different

segments and each segments holds different opinions and lifestyle.

1.1 Trust in E-commerce

The low rates of e-commerce adoption can be influenced by various factors. According to the

report from CBS (2010) on the perception of online shopping from the general population in

the Netherlands, factors such as lack of experience in doing online shopping, less trust in the

delivery, concerns with the privacy, concerns with the security, online shopping is not

necessary and preference to shop in a real shop, influence the customers attitude on online

shopping. In addition, a lack of transparency of the company offering online shopping can

also negatively impact the trust of the customers (Corbitt et al., 2003).


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Figure 1 Reasons of Dutch population for not using e-commerce (CBS, 2011)

Looking on the reasons mentioned before, it is necessary for online vendors to be able to

develop trust of the customers. Nevertheless, trust is hard to be built among the customers.

Therefore, the most fundamental aspect that the company should consider is how to build the

trust of the customers to the company.

Through this research, it is important to understand the perceptions of seniors in the

Netherlands, who are experienced with e-commerce, towards their trust in e-commerce.

People who have experience in e-commerce are more familiar with how e-commerce works.

Therefore their perceptions can be used as the basis to create a strategy to increase the trust of

seniors who are inexperienced with e-commerce.

In addition, the vendors should be able to present an e-commerce website that is secure.

Although the growth of technology has helped to overcome the barriers in e-commerce that

happen over time e.g. the payment systems, nevertheless, internet crime is also increasing

eventually because of technology e.g. stealing the data from the users, which also impacts the

trust of people in using e-commerce.

In using e-commerce, customers might experience some negative impacts from using e-

commerce websites that may affect their trust. Therefore the research will be related to


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institution based trust which relates to the trust of the customers towards the Internet

environment, which in turn can be related to the security of the website (Pavlou, 2003).

1.2 Social Influence

The second aspect that this research focuses on is the influence of people around the person

e.g. family and friends. This can be related to social system variables such as social influence.

In traditional shopping, some customers require another persons opinions prior to purchasing

the products. In addition, the peer pressure of people around the customers also influences the

adoption of a new innovation. Nowadays the Internet offers various channels that can be used

to influence users to purchase a certain product or innovations and to make the customersmore convinced before the products are being purchased. These channels for instance can be

social media or forum. Therefore, this research will also focus on understanding the social

influence that comes from the people around of the seniors.

1.3 Context of the Research

This research is conducted in corporation with Beweging 3.0.Beweging 3.0 is a company

centered in the region Eemland, The Netherlands. It is active in the field of housing, care, and

welfare with the main client group being seniors with age older than 55 (Beweging 3.0, 2012).

The mission of Beweging 3.0 is to provide good care to the customers as well as providing a

comfortable life for its customers. In addition, the company is also active in providing suitable

care, social support and assistance, and providing services to its customers (Beweging 3.0,

2012).

Within Beweging 3.0, a department called the membership service ( ledenservices ) department

has been created to focus on the external members and raising profit from these members as

up to now the organization only receives financial support from the government. Over time

the financial support of the government for the care sector is decreasing. To secure its

existence, the organization would like to expand its business through e-commerce. At this

moment, Beweging 3.0 has created a section in their corporate website that promotes services


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companies that desire to extend their business online with seniors as a target group. Based

upon the results of this research, these companies can improve their marketing strategies.

Beweging 3.0 is interested to have the results implemented in its strategy. The organization

has indicated that seniors nowadays are into technology and are becoming more active

participants in the Internet. The organization wishes in the future to create a specific e-

commerce website which targets people older than 55 years of age. Nevertheless, before

developing the website, the organization would like to understand whether the seniors wish to

purchase products in the Internet and furthermore, the organization would like to gather the

perceptions of seniors about the use of e-commerce so that the organization is able to see the

prospect of the online business in the future.

Moreover, this research projects will also bring an added value for scientific research. This

research is advancing the successful theories of Technology Acceptance Model, social

influence and trust - which have been previously tested. Furthermore, it combines these

theories into one research design and tests it into a persons usage behavior on e-commerce.

In summary, this research project is not only focusing on practical results but is also

contributing to science.

In order to achieve the goals of this research, the following research questions are proposed.

1. Which factors influence the use of e-commerce of Dutch seniors?

a. How does social influence affect the use of e-commerce by Dutch seniors?

b. How does institution-based trust affect the use of e-commerce by Dutch seniors?

c. How can we reduce the risks in using e-commerce by Dutch seniors?

In the next chapter the theories that will be used as the basis of this research will be discussed

further.


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proposes five stages that will be faced by an individual after a new innovation has been

introduced to the market and these stages are (1) knowledge, (2) persuasion, (3) decision, (4)

implementation, and (5) confirmation .

During these phases, people show certain attention to the innovation. As people show

attention to the innovation, the person develops an attitude towards this innovation, which can

be positive or negative. These attitudes are followed by the decision-making in which the

person will continue to adopt the innovation or not. If the individual decides to adopt the

innovation, (s) he will continue to implement the innovation and follows with confirmation

(Roger, 1995). Furthermore, Roger (1995) also added persuasion in the above steps and the

purpose of persuasion is to change the attitude of the customers to be positive towards the

innovation.

Furthermore, besides proposing the stages that characterize on how the process of innovation

is adopted by the customers, Rogers (1995) also stressed five characteristics of innovation,

which he refers to Relative advantage: that is related with the surplus value of the innovation; Compatibility: in which the innovation should be consistent with the existing needs,

values and expectations; Complexity: the innovation should have an image that it is easy to use; Trialability: people should be able to try out the innovation before it is fully launched

on the market; and Observability: people should be able to observe the positive effect of the product.

According to Roger (1995), when innovations fit the need of the users and match the above

characteristics, the users will adopt the innovation undoubtedly.

Each individual displays different levels of adoption when adopting a new product or

technology. The different levels of adoption are related to the personal innovativeness. Rogers

(1995) argues that the factor of adoption also comes from the personal innovativeness. He

defines the personal innovativeness as the degree to which an individual or other unit of

adoption is relatively earlier in adopting new ideas than the other members of a system (p.

22). The personal innovativeness contains of five categories of adopters. The first two groups,


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the innovators and the early adopters tend to take risks in using a new innovation. In addition,

the early and late majority is much slower than the first two groups. The last group is called

laggards that are the last decision makers to adopt an innovation (Rogers, 1995). According to

Corbitt et al. (2003) those who are active in the Internet might be more acquainted with e-

commerce. In this research, the focus will be on the perceptions of Dutch seniors who are

active on the Internet and who (may) have experience with e-commerce. This group can be

categorized as early adopters as they have tried to use the e-commerce previously.

In the next section, the discussion will focus on TAM. As it has been mentioned in the

previous section, two variables such as social influence and trust will also be discussed in the

next section.

2.3 The Technology Acceptance Model (TAM)

Fishbein and Ajzen (1975) developed the Theory Reasoned Action (TRA) model, which

includes variables such as Attitude (A), Behavioral Intention (BI), and Behavior (B) (see

Figure 2).

Figure 2 Theory Reasoned Action TRA (Fishbein & Ajzen, 1975)

In order to explain the factors that influence users in using technology, Fred D. Davis (1989)

redesigned the TRA model (see Figure 3) and named it the Technology Acceptance Model

(TAM) Davis (1989) added two determinants to his TAM model, perceived usefulness (PU)

and perceived ease of use (PEOU). He also replaced Behavior (B) by Actual System Use

(AU). Based upon earlier research on e-commerce that has implemented this model, the


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integration of this theory is expected to be useful for this research on e-commerce adoption of

Dutch seniors.

Figure 3 Technology Acceptance Model TAM (Davis, 1989)

According to Davis (1989) PU and PEoU are the determinants of attitude towards use (A).

This attitude variable together with PU predicts the Behavioral Intention (BI). When the users

have an intention to use the technology (BI), actual usage (AU) will follow.

As mentioned before Roger (1995) purposed five characteristics of innovations. Regarding

PU and PEoU, the complexity of the product or innovation can be related to PEoU and the

relative advantage in which can be related with the PU from the model of TAM model from

Davis. Rogers (1995) also stated that the intention to use the innovation would be higher if it

is suitable with the need of the users.

Each variable from the TAM model that will be included in this research will be discussed

further in the next section.

Perceived Usefulness (PU)

In this model, perceived usefulness is related to how people tend to use or not to use an

information system that leads to a better performance for them for example in job

performance (Davis, 1989). Davis (1989) defines perceived usefulness as the degree to

which a person beliefs that using a particular system would enhance his or her job

performance (p. 320). As the model was tested in the working environment, therefore the

model was developed on the basis of increasing job performance. For this research, term job

performance is defined as the daily performance as experienced by seniors doing online

shopping and the term system can be related to the e-commerce website.


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Davis (1989) also added that the perceived usefulness has strong correlation with the

behavioral intention. Gefen et al. (2003) also concluded in their research that repeat customers

or loyal customers have a stronger intention to use the website as they perceive the website as

useful, by having good experience in using the website and havingtrust in the online shop.

Nevertheless, Gefen et al. (2003) argued that those who are inexperienced in using e-

commerce require a belief behavior. When a customer develops a belief, this will lead to

attitude and intention, in which attitude can be trusting the site and having an intention to

buy, as described in the model of TRA. In addition, Klopping and McKinney (2004) also

confirmed that PU also predicts the intention to use e-commerce. Hence, it can be

hypothesized that:

H1 Perceived Usefulness is positively related to attitude towards e-commerce

H2 Perceived Usefulness is positively related to behavioral intention towards e-

commerce

Aforementioned, another determinant that was created by Davis is the perceived ease of use

(PEoU). Further information on PeOU will be described in the next section.

Perceived Ease-of-Use (PEoU)

Perceived ease-of-use is the degree to which a person believes that using a particular system

would be free of effort (Davis, 1989, pg. 320) and this variable is mentioned as the second

determinant of the intention from an individual in using a technology or information system

(IS). According to Davis (1989) PEoU is related to the channel dispositions model of

Swanson (1987) who mentioned items such as convenient, controllable, easy, and

unburdensome having influence on users choice and use of information.

Urban et al. (2009) argue that layout, typography, white space, images, and color schemes are

important in a site design. Urban et al. (2009) added that there are two design aspects that are

necessary for an e-commerce. First, a good looking-website e.g. no errors spotted on the links

and the text, right color preference, clear and well structured, right amount of information, can

make the users feel that they are dealing with a professional website. The second aspect is the

attractiveness and the usability of the website in which allowing the users to feel comfortable


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and confident in using the website and this will lead to a higher chance to perform more

online shopping. In order to convince the customers to use the website, the first impression

such as clear and well-structured presentation is important to be presented.

Furthermore, Davis (1989) also mentioned that self-efficacy theory, which is described as

judgments of how well one can execute a course of action that is required to deal with

prospective situations (Bandura, 1982, p. 122), is considered to be comparable to PEoU,

which also determines the behavior towards using an information system. Hence, users have

to have a certain level of self-efficacy in using a technology in order to be able to use an e-

commerce website.

Gefen et al. (2003) also stressed the perceived ease-of-use of an e-commerce website. The

more users are familiar with operation of the website, the more users will have an intention to

stay as a repeat customer. Customers feel comfortable when they enjoy using the website that

has a good usability and also a good look, which creates an intention of purchasing more

products in the website. Although Gefen et al. (2003) stated that PEoU is positively related to

the intention to use e-commerce, Pavlou (2003) stressed that the PEoU is not significantly

influencing the customers intention to transact.

In relation to attitude, Heijden (2002), in his research about the factors that influence the use

of generic portals in the Netherlands, stated that PEoU is positively related with the attitude of

Dutch population towards the use of a portal website. Therefore, it can be hypothesized that:

H3 Perceived Ease-of-Use is positively related with the attitude towards e-commerce

H4 Perceived Ease-of-Use is positively related with the intention to use e-commerce

H5 Perceived Ease-of-Use is positively related with Perceived Usefulness

Attitude (A)

According to Fishbein and Azjen (1975), the attitude is the first important variable in the

Theory of Reasoned Action (TRA) and this attitude can be positive or negative. Venkatesh et

al. (2003) describe attitude toward using technology as an individuals overall affective

reaction to using a system (p. 455). In the TAM model, Davis (1989) applies the attitude that

becomes one of the determinants of the behavioral intention to use the technology, which is


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related to the desire of people to use the system. Lu et al. (2003) also confirmed that the

attitude is influencing the behavior intention towards using an information system (IS).

Although most of the previous research mentions that attitude directly influences the

behavioral intention, Venkatesh et al. (2003) argued that attitude is not necessarily a direct

determinant of the intention to use an information system (IS). The attitude does not always

directly influence the intention to use the product as users with no positive attitude towards

the IS may still use the technology to help them to perform their job better (Davis, 1989). As

the research from Venkantesh et al. (2003) was also conducted in a working related

environment, the results may be different when it is applied in daily life.

Aforementioned, attitude has been mentioned to be not a direct determinant of Behavior

Intention (BI). Hence Venkatesh et al. (2003) in their research about User Acceptance of IT

also mentioned that a specific attitude e.g. positive attitude towards an innovation could be

determinant of motivation and behavior intention. These attitudes are followed by the

decision-making of the customers whether the innovation will be adopted or not.

Behavioral Intention (BI)

This intention to use can come from the Perceived Use (PU) or from the Attitude (A) of the

users. Regarding this variable, Fishbein and Azjen (1975) also argue in their Theory Reasoned

Action model that behavioral intention will only happen when the attitude of the person

towards an object is there. This attitude can be related to evaluation of the object and also the

belief strength towards the object. Therefore, for attitude and behavioral intention, the

hypothesis that can be proposed is:

H13: Attitude positively influences behavioral intention

2.4 Social Influences (SF)

An individuals choice to adopt a product is not only determined by the experience that the

individual has, as it can also be determined by social influence. The interaction between the

users with their social network can enhance other users to adopt the e-commerce. As one will


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influence another to use the product that one considers as good. Moreover, the peer pressure

from the users can also influence the users to start adopting an innovation.

Roger (1995) mentioned that early adopters tend to use a product after other people

recommend a product that they have used. It can be perceived that there is a communication

process between the individuals. According to Dainton and Zelley (2011), the communication

process refers to the flow of information from one person to another (p. 2). This flow of

information can also be seen as a word of mouth.

Roger (1995) also argues that the transfer of a new idea can be developed through mass media

channels and interpersonal channels. By using mass media channels, the new idea is

transferred by mass media, for example newspaper, television, and radio. In addition,

interpersonal channels also play a role in transferring the new idea, through face-to-face

contacts. Roger also added that the Internet has also a role in transferring new innovations

nowadays. Although mass media play a great role in creating knowledge about an innovation,

nevertheless, interpersonal communication also plays important role on transferring the

information on the experience of using the innovation.

Venkatest et al. (2003) developed an extension model of TAM that was named as UTAUT. In

this model, the social influence variable was added. According to Venkatesh et al. (2003)

social influence is described as the degree to which an individual perceives that important

others believe he or she should use the new system (p. 451) .

Venkatesh et al. (2003) also mentioned that social influence has a direct impact on the

behavioral intention to adopt a technology without attitude as the mediator.

Nevertheless, Malhotra and Galleta (1999) found that social influence did not have any

significant relation with the behavioral intention when they tested the variable with TAMmodel. Moreover, Kulviwat, Bruner and Shuridah (2009) have also proved on their research

that social influence has become one factor that motivates the intention from the users to

adopt a high-technology product. Nevertheless, Kulviwat, Bruner, and Shuridah (2009)

considered attitude to be the mediator of social influence and intention, nevertheless it has a

weak direct link compare to the direct correlation between the social influence and intention

to use.


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Social influence factors can be useful in many areas. Various researches recommend to

companies to focus on social influence variables to convince the potential customers that

other customers have satisfaction in using the product ( Kim & Srivastava, 2007; Kulviwat,

Bruner, and Shuridah, 2009). Kim and Srivastava (2007) recommend website vendors to

focus on social influence. The customers can obtain this influence from their social network,

which can have impact on the customer decision-making before buying a product from a

vendor. In addition, Urban et al. (2009) also relate the social influence factor as word-of-

mouth that can increase the trust of other users, which will increase ones motivation to use e-

commerce. Hence, it can be hypothesized that:

H6: Social Influence is positively related with attitude towards e-commerce

H7: Social Influence is positively related with behavioral intention

2.5 Trust

Trust is the foundation of a business, particularly of an online business. When the trust from

customers has been vested into the company including its website, there is a chance that

customers will remain loyal to the company. In general, trust itself has been described as ageneralized expectancy held by an individual that the word of another can be relied on

(Farhoomand and Lovelock, 2001, p. 337). Gefen et al. (2003) also argued that trust is about

the expectation from an individual to others, either individuals or businesses, that there will be

no advantage taken from them. Therefore, building trust is the first step that the e-commerce

vendors need to perform.

Building trust with customers is an important factor for an e-commerce vendor, as face-to-face interaction with the sellers is less applicable and moreover, judging the quality of the

product is more difficult in e-commerce. This can lead to uncertainty, vulnerability and

dependence, which according Corbitt et al. (2003) is the characteristics of distrust.

Trust in e-commerce can be related to online trust. Online trust is the trust that the consumers

have in an e-commerce business, particularly on how e-commerce websites would deliver the

product as promised, the trustworthiness of the information that they provide, and how

confident the site commands are (Bart et al., 2005; Wang and Emurian, 2003). When the


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added that a clear privacy policy and clear general information in the website is important to

build and maintain trust from the customers.

Gefen et al. (2003) define the factors that have been mentioned by Farhoomand and Lovelock

(2001) as institution-based trust. According to Gefen (2003), trust can be generated from

institution-based trust and it refers to trust emanating from the security, one feels about the

situation because of guarantees, safety nets, or other structures (p. 318) and this can be

related to the trust logo, privacy policies, guarantees, affiliations with respected companies

and contact us buttons (Gefen, 2002; Luo, 2002).

McKnight et al. (2002) mentioned institution-based-trust as the belief that the structural

conditions are present (e.g., in the Internet) to enhance the probability of achieving a

successful outcome in an endeavor like e-commerce (pg.339). In addition, institution-based

trust is considered to consist of two factors such as 1) structural assurance and 2) situational

normality. According to McKnight et al. (2002), quoting from Shapiro (1987) and Zucker

(1986), defines structural assurance by stating that in order to reach success, there should be a

structure such as guarantees, regulations, promises, legal recourses or other procedures. This

can be achieved by providing data encryption that safeguard one from loss of privacy,

identity, or money. Moreover, situational normality is considered to be the belief that the

environment - which can be defined as the Internet - is in good order and considered as a

normal or favorable situation for customers (McKnight, 2002). Therefore, institution-based

trust elements need to be implemented in the website to increase the trust of the customers.

In addition to the two factors structural assurance and situational normality, trust itself can be

based on three components: 1) an ability or competency that the vendor is performing well on

the business, e.g. providing quality products and service; 2) benevolence, that is whether theorganization also takes the customer interest into account, not only the company interest; and

3) integrity or confidence as the vendor ensures to perform what the vendor should do (Salam

et al., 2005; Urban et al., 2009; McKnight et al., 2002). These three components that construct

trust are important to be considered by the companies who are willing to keep their customers.


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Figure 4 Website Trust Model (McKnight et al., 2002)

From Figure 3, it can be seen that institution-based trust is related to disposition to trust,

which can be related to general trust characteristics of the individual (McKnight et al., 2002;

Gefen et al., 2003). Nevertheless, this research is focused on the characteristics of external

factors, with other words every aspect that is institutionalized, e.g. the existence of a privacy

statement, a secure logo, etc., which can influence the trust belief from the seniors which can

influence their behavioral intention to use the e-commerce website. In the trust architect from

Urban et al. (2003) the institution-based trust can be categorized in the Internet block and it

contains of the perception from an individual towards the Internet environment. This will

create trust to vendors that consist of confidence or integrity, competence, and benevolence.

When there is trust, this will lead into the intention to buy. Figure 4 explains the trust

architecture according to Urban et al. (2003):


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Figure 5 Trust Architect (Urban et al., 2003)

McKnight et al. (2002) combined the variables of institution-based trust and basics of trust

that consist of benevolence, integrity and competence into five variables, which are

Situational Normality-General (IG), Situational Normality-Benevolence (IB), Situational

Normality-Integrity (II), Situational Normality-Competence (IC), and Structural Assurance

(ISA). Each of these variables will be related to the behavioral intention to use e-commerce

which can be interpreted as how Dutch seniors see the Internet environment and whether thisinfluences their intention to use e-commerce. Hence, the hypotheses that can be proposed are:

H8: Situational Normality-General (IG) is positively related with behavior intention

H9: Situational Normality-Benevolence (IB) is positively related with behavioral

intention

H10: Situational Normality-Integrity (II) is positively related with behavioral intention

H11: Situational Normality-Competence (IC) is positively related with behavioral

intention

H12: Structural Assurance (ISA) is positively related with behavioral intention

Risk Reduction Strategies


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Consequently, in order to increase the trust from the customers, there should also be a strategy

to reduce the risk from using e-commerce. Corbitt et al. (2003) mentioned several risk

reduction strategies that can be implemented to reduce the fear of using e-commerce that can

be faced by the users. Sheth and Venkatesan (1968) mentioned that as the customers

perceived some risk in buying or adopting an innovation, they would try to reduce it by

having references from people around them. Corbitt et al. (2003) added that positive word of

mouth, money back warranty and partnerships with well-known business partners are the

most needed by the customers in order to decrease the perceived risk and create more trust in

the vendor.

Pavlouv (2003) mentioned that trust and risk have influence in the customers acceptance

towards e-commerce. Pavlouv (2003) also added that risk reduction would increase the

acceptance of the customers on e-commerce. Therefore, in this research the institution-based

trust, consist of five variables, Situational Normality-General (IG), Situational Normality-

Benevolence (IB), Situational Normality-Integrity (II), Situational Normality-Competence

(IC), and Structural Assurance (ISA) will be related with the risk reduction items that have

been created by Corbitt et al. (2003).

This literature review will be used as a basis of conducting the research targeted to the Dutch

seniors population. Moreover, by applying these theories, the research will be more structured

and will also be able to provide a recommendation for the organization that is strongly based

on theory.


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3 Research Methodology and Research Design

3.1 Model

To be able to answer the research questions, some hypotheses have been created and in this

section, a model is presented that visualizes the research work.

As mentioned before, the research is focusing on the TAM model from Davis (1989), on trust

with a focus on institution-based trust, and on social influence variables. The TAM model was

originally developed to test the perceptions of workers towards the use of computers in the

work settings (Davis, 1989). The purpose of this research is to analyze the use of e-commerce

in daily live. For this purpose the items that will be applied in this research will be related to

the TAM, whereby the use of e-commerce is related to the use of computers in the original

TAM research, and whereby seniors in daily life are related to workers in an organizational

setting in the original TAM research.

In addition to TAM, institution-based trust and social influence variables, the items used in

the survey also included risk reduction items. Besides understanding Dutch seniors actual


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perceptions towards e-commerce, in this research, some questions about their preferences on

an e-commerce website are also presented. Hence the result of this research will lead not only

to theoretical results but also to practical results.

3.2 Instrument

The instrument of this research is the survey method. The survey method offers several

advantages; such as (1) it can be filled in at a convenient time for the respondents, (2) the

respondents can take their time to fill in the survey, (3) as the respondents are informed that

the survey is anonymous, sensitive questions can be provided to the respondents which might

be difficult to explore in other methods such as interviews and focus groups (Downs andAdrian, 2004).

An additional benefit of choosing the survey method for this research is that it can be used to

measure the needs of the users and also their attitude to new system ideas. In addition, by

using the survey method, the researcher can obtain a large number of respondents in a short

time (Maguire, 2001).

According to Downs and Adrian (2004), there are two types of survey, 1) a newly designed

survey, and 2) using the survey that has been created by another author. The items that are

used for this research are original items from the creators of the theories or models and are

related to the context of e-commerce.

The self-administrated survey contains 56 items that measure the opinions of Dutch seniors

toward e-commerce. With the objective of creating a more focused research, items related toactual use and website appearance were removed from the analysis and these items are no

longer important for this research. Therefore, at the end the focus of this research is only on

the TAM items created by Davis (1989) with two variables such as Perceived Usefulness

(PU) and Perceived Ease-of-Use (PEoU). In addition, items on social influence, attitude and

behavioral intention as created by Venkatesh (2003) were added. Next to these items, the

items for institution-based trust are borrowed from McKnight et al. (2002). For the risk

reduction items have been obtained from Corbitt et al. (2003). The reliability and validity of

these items has been previously tested. Nevertheless, as the items are now tested to a different


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population, the items wording has been adjusted so that these are more related to the topic of

this research.

The items in the survey implement the self-report survey that measures the respondents

attitude, feeling, and belief toward e-commerce and it uses Likert style questions on a 5 point

scale (1= strongly disagree, 2=disagree, 3=neutral, 4=agree 5=strongly disagree). The self-

report survey has been used in various adoption researches particularly in the field of e-

commerce (Corbitt et al. 2003; Gefen et al, 2003). Moreover, the respondents filled in the

survey based on their previous experience in using e-commerce in general.

As the survey was distributed to the Dutch population and in order to reach higher response,

therefore the survey was created in Dutch. The Dutch version of the survey can be found in

the appendix section.

3.3 Procedure

The first step of this research was to conduct a small pilot study. The purpose of conducting

this pilot research is to make sure that the respondents were able to answer the questions

provided and also to increase its reliability. The pilot research that was conducted before the

main distribution of the survey was first tested to three experts in social research and followed

by testing it to three representatives from the sample. The feedback that was retrieved from

the pilot research was mostly related to the structure of the language as Dutch is not the first

language from the researcher.

After the survey was reviewed, the survey was distributed to respondents starting from 15 th of

June 2012 until 31 st of July 2012. In order to generate a high response rate, the survey was

administrated both online and offline. In the online administration, the link to the survey was

attached in the email and the email was sent out to the potential respondents. Hence, the

respondents were able to fill in the survey online. Additionally, the survey was also

distributed offline in which the respondents were able to fill in a paper-and-pencil type of

survey.

It is necessary to obtain response from a large number of respondents in order to be able to

adequately generalize the result. Therefore, the selection of the sample was based on


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convenience sampling, snowball sampling and also from the panel provided by Thesistools.

As there was initial difficulty to find respondents online, the convenience sampling was the

first method that was applied. The convenience sample is defined as a nonprobability

sampling method that depends on self-selection and it depends on the availability of the

respondents (Dooley, 2008). This sample was selected from the public space, in Zandvoort

beach boulevard, the Netherlands. Before distributing the paper-and-pencil survey, the

potential respondents were asked whether they had experience with e-commerce. When the

respondents confirmed and were also willing to fill-in the survey, they could fill the survey

immediately on the place. The advantage from getting the respondents in this location is that

the respondents could take their time to fill in the survey. The disadvantage of implementing

this method in this location is that it is labor intensive and there might be some potential

respondents missed because of lack of interest in being surveyed in this way or because they

do not have experience in using e-commerce. In total 10 respondents filled in the survey.

In order to enlarge the sample, snowball sampling method was also implemented. Snowball

sampling is a technique of getting respondents from the acquaintances of the respondents of

the research. In this way, the online survey was distributed to acquaintances and these were

requested to forward link to the survey to their acquaintances that have the same demographic

characteristics as them.

The sample that filled in the survey online was also selected from a panel provided by

Thesistools. To obtain a sample, the email that was sent to the panel stated that the

respondents were required to have experience in performing online shopping. Furthermore,

the panel from Thesistools enables the user to make a preselection on ageand the residence of

the respondents. In this case people aged above 55 years old and living in The Netherlands

were selected. Therefore, the respondents fit the requirements that have been developed forthis research. The sample that completed filled the survey was rewarded with 1 euro. From

this panel, 100 people were invited by email to fill in the survey.

The panel offers different kind of benefits. Firstly, by using the panel, various types of

participants from the population with for instance different ages, genders, and backgrounds,

can be obtained in a short time. Second benefit is that the respondents are spread throughout

the Netherlands. Third benefit is that by using this panel, the author is able to generate


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response from the population without experiencing response bias which can occur when the

respondents know the background of the researcher (Downs & Adrian, 2004).

As the data collection was administrated online and offline, therefore in order to avoid errors

in the dataset, all records from the survey were collected in one database provided by

Thesistools. By using the database from Thesistool, the resulting data could be analyzed

easily.

In order to get more significant results, the respondents, which entered a meaningless survey

response were not taken into account and at the end of the survey, 125 respondents were

collected that had fully completed the survey.

3.4 Population and sampling

The sample of this research was collected in The Netherlands. The respondents consisted of

Dutch seniors who are active in the Internet, who are able to operate media such as a

computer, laptop or tablet pc, and who also had experience with e-commerce.

The sample of this research is the senior population which is widely spread in The

Netherlands having various genders, ethnical backgrounds, income situations, and educational

backgrounds. According to CBS (2012), the total population of the seniors in the Netherlands

is about 4.891.400 people and this is around 29% of the total population in the Netherlands.

In addition, the respondents in this research were aged 55 years and above. From this age

selection, there will be 3 groups generated with age range between 56 until 64, age range between 65 until 73, and age range above 74 years old. The age grouping is based on the

grouping that has been done by Pew Research Centre (2010) that segments the mature group

into three groups.

This research has been able to generate 125 respondents, with male (n=77) and female (n=46).

The description of the respondents related to their gender, age, educational background,

income, Internet use, and e-commerce use are shown below


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Table 1 Descriptive statistic of respondents characteristic (N=125)

Frequency Percentage

Age

56-64 40 32%

65-73 75 60%

74 or older 10 8%

Gender

Male 77 61.6%

Female 46 36.8%

Unknown 2 1.6%

Education

Lower Education 13 10.4%

High School Education 27 21.6%Higher Education 85 68.0%

Internet Use

Less than 1 hour per day 9 7.2%

Between 1 until 3 hours per day 79 63.2%





More than 15 hours per day 2 1.6%

Missing 1 0.8%

Income per month (gross)

2000 Euro or less 17 13.6%

2001 Euro - 2500 Euro 15 12.0%

2501 Euro - 3000 Euro 18 14.4%

3001 Euro - 4000 Euro 24 19.2%

4001 Euro or more 36 28.8%

Unknown 15 12.0%

Frequency of doing online shopping

Less than 1 time per month 75 60%

Between 1 until 3 times per month 35 28%

Between 3 until 5 times per month 13 10.4%

More than 10 times per month 2 1.6%

In general, the average the sample of this research was mostly male, from the age groups of

65 until 73, mostly well educated as most of them obtained higher education degree and earn

about more than 4000 euros per month/gross.


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In relation to gender, according to CBS on 1 January 2012 the Dutch female senior population

was 2.6 million (53%) and the Dutch male senior population was 2.3 million (47%). The

sample here has 61.6% male and 36.8% female.

In relation to education, according to CBS in 2011 the Dutch population aged 55 65 years

had lower education background 13%, high school education 60% and higher education

background 26%. In this sample, the majority of the sample has a higher education

background (68.0%), followed by high school education (21.6%) and lower education

(10.4%). While according CBS in the population the Dutch seniors are mostly only obtained

lower education background rather than the higher education background. Hence, the data is

skewed towards people with higher education background.

In summary, it can be seen that the dataset is skewed by gender towards male and skewed by

education towards people with a higher education background.

In relation to income, 28.8% of the respondents received more than 4000 euros gross/month

followed by 3000-4000 euro gross/month, in which is received by 19.2% of the sample.

In relation to the use of the Internet and online shops, 63.2% of the samples are active in the

Internet for one until three hours per day and followed by three until five hours per day. In

relation with the use online shops, the samples mostly makes less than one time per month use

of an online shop (60.0%), followed by one until three times per month (28.0%). It can be

concluded that these sample are quite moderate users of the Internet and also moderate users

of e-commerce.


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4 Results

In this chapter the result of the survey that has been distributed will be discussed. The data has

been processed using SPSS. There are several methods that will be used to analyze the data.Cronbachs alpha will be applied to analyse the reliability of the items used, Pearson

Correlation Coefficient will be applied to analyse the strength and the direction of one

variable to another variable, or between each item to a variable and Regression lines will be

used to analyse which variables become the most important predictors of the dependent

variables.

4.1 Reliability Analysis

The first step of the data analysis is to measure the internal-consistency reliability of the items

and also to measure the power of each variable that will be included on the model test. The

items that were used for this research and the total score for Cronbachs alpha are described

below:

Table 2 Cronbachs alpha for each variable of the model

Variable Total ItemsCronbachs

alpha

Perceived Usefulness (PU) 3 0.683

Perceived Ease of Use (PEoU) 3 0.796

Social Influence (SF) 5 0.833

Situational Normality-General (IG) 2 0.850

Situational Normality-Benevolence (IB) 2 0.732

Situational Normality-Integrity (II) 3 0.791

Situational Normality-Competence (IC) 3 0.860

Structural Assurance (ISA) 4 0.830

Attitude (A) 3 0.902

Behavior Intention (BI) 2 0.740

It can be seen in Table 2 that the alpha from each variable is above 0.60, which implies that

each item that is used in this research is internally consistent. This means that the items are

measuring the same construct.


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4.2 Descriptive Statistics

Following the reliability analysis, the next step is to understand the means, standard

deviations, and minimum and maximum from the items and variables that have beenmeasured with the five points scale. Table 3 presents the variables that are important for the

model

Table 3 The Main Variables for the model, descriptive (N=125)

Variable N Minimum Maximum Mean Std. Deviation

Perceived Usefulness 125 1.00 5.00 3.3387 .86238

Perceived Ease of Use 125 1.00 5.00 4.0933 .83666

Social Influence 125 1.00 4.60 2.0704 .77595

Situational Normality General 125 1.00 5.00 3.1440 .86326

Situational Normality

Benevolence

125 1.00 4.00 2.6400 .71165

Situational Normality Integrity 125 1.00 5.00 3.5653 .70866


Competence

125 1.00 5.00 3.3547 .69913


Assurance

125 1.00 4.25 2.8760 .72026

Attitude 125 1.00 5.00 3.3893 .90868

Behavioral Intention 125 1.00 5.00 3.2600 .93671

From Table 3, it can be seen that PEoU has the highest mean in comparison to the rest of the

variables ( M = 4.0933, SD = 0.83666 ). Situational Normality Integrity has the second highest

mean in here ( M =3.5653, SD= 0.70866 ). The smallest number can be found on the mean

from Social Influence ( M= 2.0704, SD=0.77595 ).

The Social Normality Competence has the lowest standard deviation ( M = 3.3547, SD =

.69913 ). This can be because there is not a big difference in the score between the respondents

in this sample and the data is spread around the mean.

4.3 The Correlations Matrix for the Model

Prior to the regression analysis, the next step after the descriptive statistic analysis is to check

on the strength and the direction of the variables and also of the items using Pearson product-


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moment correlation coefficient. Table 4 provides a summary of the correlation coefficients

between the variables that based on the hypothesis have been related and Table 5 provides the

correlation matrix of each variable. The numbers that are marked in bold are important for

this research.

Table 4 The hypothesis and correlations from each variable

Hypothesis Correlation

H1 PU Attitude

H2 PU - Behavior Intention

H3 PEoU Attitude

H4 PEoU - Behavior Intention

H5 PEoU - Perceived UsefulnessH6 Social Influence Attitude

H7 Social Influence - Behavior Intention

H8 IG - Behavior Intention

H9 IB - Behavior Intention

H10 II - Behavior Intention

H11 IC - Behavior Intention

H12 ISA - Behavior Intention

H13 Attitude - Behavior Intention

Table 5 The correlations between models variables

Variable number 1 2 3 4 5 6 7 8 9 10

(1) Behavior Intention 1

(2) Attitude .594 1

(3) Perceived Usefulness .474 .646 1

(4) Perceived Ease-of-Use .384 .422 .441 1

(5) Social Influence .066 -.015 .073 -.204 1

(6) Situational Normality General .477 .536 .508 .420 .082 1

(7) Situational Normality Benevolence .293 .289 .386 .057 .114 .448 1

(8) Situational Normality Integrity .487 .414 .407 .492 -.193 .499 .410 1

(9) Situational Normality Competence .398 .441 .330 .380 -.228 .509 .478 .601 1

(10) Situational Normality Assurance .329 .466 .408 .222 .055 .444 .573 .531 .476 1

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

*. Correlation is significant at the 0.05 level (2-tailed).


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The first relation is between Perceived Usefulness (PU) with Attitude (A) ( r = .646, P

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