Development Of Modern Wireless Communication Technology

Published Date: 02 Nov 2017

1.0 Introduction

This chapter will cover an introduction on this research and provide an overview on research background, problem statement, research objectives, research questions, hypothesis and significant of study.

1.1 Research Background

The drastically changing development of modern wireless communication technology, coupled with the increasingly high usage of internet is promoting mobile shopping (MS) as a significant utility and application for both organizations and society in the digital world (Pascho, Sunderam, Varshney and Loader, 2002). The rapid development of popularity in mobile devices and acceptance of smartphones in recent years has attracted the attention of consumers. With the vigorous development of communication and advance technology, mobile shopping has become one of the critical consumer behaviour that practice by public.

With the accelerative competition of technologies and the increasing figure of using internet and wireless devices, therefore there is an important need to comprehend the factors that entice customers to practice mobile shopping. Understanding the importance of what determines the society in practicing mobile shopping can lead to more favorable strategies that make the organizations to remain rivalry.

The wider the connection of a business can bring to the target market, the higher possible sales they can make. By having this, maximize the benefits from the latest technology such as mobile devices that let society practices mobile shopping has brings to a step forward to a more advance country. Mobile Shopping brings many advantages that will benefits the society. These advantages include convenience, flexible accessibility and time efficient.

1.2 Problem Statement

Technological advancement especially in mobile devices which has attracted consumers has basically altered the way consumers shop for their belongings (Wong, Lee, Lim, Tan and Chua, 2012). Mobile shopping provides advantages such as convenience whereby consumers are not required to drive to shopping mall. Time saving and even the improvement of the quality of life eventually increases the number of mobile users.

According to Ant Ozok and Wei (2010), online shopping has no longer just restricted to desktop computers and even the wired connections. Apart from that, as the number of mobile users getting increase, this creates a new opportunity for dealers to increase revenue (Kuo and Yen, 2009).

Although m-shopping provides several added benefits to consumers, however, many of consumers do not fully make use of the mobile for shopping. For example in Malaysia, Malaysian Communication and Multimedia Commission (MCMC) (2010) found out that only 8.8 percent of consumers eventually made payments by using their mobile phones despite having more than 10,335,000 3G subscribers.

The main purpose of this research study is to figure out the theoretical foundation that influencing m-shopping adoption. With such a great number of mobile phone penetration, but the percentage of m-shopping adoption in Malaysia remains low and through this, formulating effective strategies are essential to increase m-shopping in Malaysia consumers.

1.3 Research objectives

The purpose of this research is to evaluate on how several variables such as innovativeness, compatibility, social influence can modify the influence of Technology Acceptance Model (TAM) variables on the adoption intention of mobile shopping.

1.3.1 General objective

Researchers are examining the impact of innovativeness, compatibility, social influence, TAM beliefs (ease of use and usefulness) on mobile shopping adoption in Malaysia.

1.3.2 Specific objectives

To determine whether there is significant relationship between Innovativeness with the adoption of mobile shopping among mobile users in Malaysia.

To determine whether there is significant relationship between compatibility with the adoption of mobile shopping among mobile users in Malaysia.

To determine whether there is significant relationship between social influences with the adoption of mobile shopping among mobile users in Malaysia.

To determine whether there is significant relationship between perceived usefulness with the adoption of mobile shopping among mobile users in Malaysia.

To determine whether there is significant relationship between perceived ease of use with the adoption of mobile shopping among mobile users in Malaysia.

1.4 Research Questions

The research project is to identify several factors that affect the adoption of mobile shopping among mobile users in Malaysia. Several variables (innovativeness, compatibility, social influence) and TAM beliefs (ease of use and usefulness) are major factors of mobile users’ intention to adopt mobile shopping in Malaysia.

Is there any significant relationship between innovativeness with the adoption of mobile shopping among mobile users in Malaysia?

Is there any significant relationship between compatibility with the adoption of mobile shopping among mobile users in Malaysia?

Is the any significant relationship between social influences with adoption of mobile shopping among mobile users in Malaysia?

Is there any significant relationship between perceived usefulness with adoption of mobile shopping among mobile users in Malaysia?

Is there any significant relationship between perceived ease of use with adoption of mobile shopping among mobile users in Malaysia?

1.5 Hypotheses of the study

H1: There is a positive significant relationship between innovativeness and the intention to adopt the mobile shopping among mobile users in Malaysia.

H2: There is a positive significant relationship between compatibility and the intention to adopt the mobile shopping among mobile users in Malaysia.

H3: There is a positive significant relationship between social influences and the intention to adopt the mobile shopping among mobile users in Malaysia.

H4: There is a positive significant relationship between perceived usefulness and the intention to adopt the mobile shopping among mobile users in Malaysia.

H5: There is a positive significant relationship between perceived ease of use and the intention to adopt the mobile shopping among mobile users in Malaysia.

1.6 Significance of the study

The used of mobile phone have increasing rapidly over this few years in Malaysia. The factors of innovativeness, compatibility, social influences and TAM model are being examined that could lead to m-shopping. The further advancement of technology in mobile devices have enhanced m-shopping as a useful tool for consumers.

M-shopping allows consumers to shop anytime and anywhere without their physical presence in a particular store. Price comparison, purchase ordering and even paying are more convenience where mobiles services can have all the transition.

In this research, we mainly focus on three variables which is the innovativeness or degree of interest in trying a new concept, compatibility or in other word, the degree that past experiences, needs of potential adopters to which an innovation is perceived and social influences where family, relatives, friends play an important roles that can encourage consumers to adopt m-shopping.

TAM model is being used as the foundation model in relation to which the influence of three variables that mentioned above is evaluated.

1.7 Chapter Layout

This research consist a total of three chapters where a brief introductory of the researching topic will be discussed in the first chapter, and then followed by an elaboration of its relevant literature review in the next chapter. Next in the third chapter, a sample research method adopted by previous studies will be discussed as well as a reliable testing on its questionnaire. Chapter Four contains of data and sampling collection including statistics and charts which show the results of this study while Chapter Five is the windup of the overall research which has been conducted.

1.8 Conclusion

In this chapter, researchers discussed about the research background, problem statement, research objectives, research questions, hypothesis of the study, significance of the study and outline of the continuous chapter. In Chapter Two, literature review to support this research will be conducted.

CHAPTER 2: LITERATURE REVIEW

2.0 Introduction

Customers’ attention towards mobile devices & availability of smartphones has been increasing in recent years. Due to this rapid growth, M-shopping has placed mobile retailers at customers’ fingertips and nevertheless allowed mobile users to shop anytime, anywhere without leaving their houses (Megdadi and Nusair, 2011).

Thus, M-shopping enables consumer actively engaged and intend to make more purchases by offering convenience and also flexibility of mobile services. Many researchers in academy and even industry specialize have actively participated in this study as technology integration have become part of modern life and leads to economy changes (Kulviwat, Bruner and Al-Shuridah, 2009).

M-shopping has been viewed as one of highly potential growth in the current market (Chen and Yang, 2012). According to Ha and Stoel (2009), customers mostly integrate M-shopping quality, enjoyment and trust into the acceptance of M-shopping.

In this paper, three variables that being taken into study is innovativeness, compatibility, social influence and together with a TAM model on how those variables could eventually affect consumers to adopt M-shopping.

2.1 Review of Literature

2.1.1 Innovativeness (IN)

Innovativeness is the willingness of an individual to try out a particular new product in the market and is often related to individual time of adoption (Rogers, 1995). Many past studies have proofed the influence of innovativeness on intention (Agarwal & Prasad, 1998). According to Agarwal and Prasad (1998) also found that individual level of innovative have higher values in developing a positive perception on new technology.

2.1.2 Compatibility (CO)

Rogers (1995) defined compatibility which a product innovation is consistent with the past experiences and also have the new possible adopters. The usages of mobile phones are getting common nowadays. Previous experience from consumers using internet as the main shopping tools can be also assessed by mobile phones for the same function. This compatibility concept has been used to study the acceptance of M- internet (Cheong & Park, 2005), e-commerce (Eastin, 2002) and mobile commerce (Yang, 2005).

2.1.3 Social Influence (SI)

According to Pedersen and Ling (2002), social influence is one of the important variables in any adoption model. Therefore, it will not be a surprise if social influence can be widely accepted in mobile banking adoption (Riquelme & Rios, 2010), internet banking (Chan & Lu, 2004) and online games (Hsu & Lu, 2004). Riquelme and Rios (2010) even explained that the suggestions especially from friends, family and even relatives play an essential role in making a decision to adopt a new technology.

2.1.4 Perceived Usefulness (PU)

PU is being defined as a form of extrinsic motivation where an individual believes that when using the particular new system, one’s job performance will be enhanced (Davis, 1989). PU is being identified as an important element which has an impact on intention to use IT. This issue is greatly supported by most of past researchers who tried to identify the factors leading to adoption of IT (Argarwal & Prasad, 1999). Arjen and Fishbein (1980) assume that people consider the impacts of their possible actions and make decisions to do the tasks based on their reasoning.

2.1.5 Perceived Ease of Use (PEOU)

PEOU has the definition of a prospective adopter’s that believes that using a particular new system would be free of effort (Davis, 1989). In other words, users are able to use the specific system without any burden. The nature of innovativeness or a task or services related to it also influence the perception on PEOU (Fang, Chan, Brzezinski & Xu, 2003). The innovation on mobile technology tends to be more functional and simple to use as a trend. For instance, a simple repetition on a system reduces the effort required and improves the task performance (Alban & Hutchinson, 1987).

2.2 Review of Relevant Theoretical Model

Figure 2.0: Relevant Theoretical Model

Perceived Usefulness

Actual System Use

Behavioral Intention to Use

Perceived Ease of Use

Source: Technology Acceptance Model (Davis, 1989)

In figure 2.0, The Technology Acceptance Model (TAM) is one of the most widely adopted models for the research that focus on IT adoption. According to Davis (1989), there are two determinants towards the main reason behind the adoption of a new technology that is perceived ease of use (PEOU) and perceived usefulness (PU). Basically, PEOU refers to free from burden from using a new system or technology whereas PU is where a person believes that using a new technology have an impact to enhance his or her job performance. As a result, this model will eventually influences behavioral intention to use and subsequently the actual usage.

2.3 Conceptual Framework

Figure 2.1 Conceptual Framework

Innovativeness

Compatibility

Social Influence

Intention to use

Perceived Usefulness

Perceived Ease of Use

Source: Developed for this research

2.4 Hypothesis Development

2.1.1 Innovativeness (IN)

The consumer innovation refers to the consumer himself being willing to adopt any new product or ideas (Citrin & Sprott, 2000). Agarwal and Prased (1998) described the personal innovativeness symbolizes certain people of existence to favour risk exposure and not in others. They can deal with the high level of uncertainty and toward accepting by more positive intention (Rogers, 1983). When the people decide to adopt the new technology mobile services, most of them do not have much information about it. It is expected that individual innovativeness will share out the information based on their experiences of perceived usefulness and perceived ease of use (Bhatti, 2007).

The adopter need to passes through some of the stages before they made the decision whether need to adopt or reject the innovation (Saljoughi, 2002). According for Rogers (2003), five steps are involved in the innovation-decision process involves which are knowledge, persuasion, decision, implementation, and lastly confirmation.

Knowledge: It has three individual generalizations which are socioeconomic characteristics, personality variables and communication behaviour (Saljoughi, 2002). Before the consumer adopt a new technology, the already has some idea about the functions of that new thing (Rogers, 1995).

Persuasion: The decision-making unit configuration a view regarding the innovation. This opinion could be good or bad. In this stage is mainly based on feelings. It is more concentrate in the interpersonal channels. In this stage we perceived the characteristics of the innovation are: relative advantage, compatibility, complexity, trial ability, and observe ability.

Decision: In this stage, the decision making unit need to decide whether need to adoption or rejection the innovation.

Implementation: In this stage, the decision making unit an innovation will put in to practices. They start to experiences the new product. Some of the uncertainty problem about the result of the innovation still can be exist at this stage.

Confirmation: At the last stage the decision-making unit need to decide want to his confirms or reverses the decision to reject or adopt the innovation that made in the previous 4 stage. The reasons for the adaptor change their minds in this stage is maybe the information of their received in previous stage about innovation may have conflicted with the early beliefs.

Figure 1.0. A Model of Five Stages in the Innovation-Decision Process (Source: Diffusion of Innovations, Fifth Edition by Everett M. Rogers. Copyright (c) 2003 by The Free Press. Reprinted with permission of the Free Press: A Division of Simon & Schuster.)

The finding from Sullivan and Drennan (2007) in Australia that adopting university students as the respondents revealed that IN has a positive relationship to adopt M-services. Past research also have the same finding in which the researchers conclude that individuals with higher perceived innovativeness eventually will have a better positive perception on new innovation (Lopez-Nicolas, Molina-Castillo & Bouwnan, 2008)

H1: Innovativeness has a positive significant relationship with M-Shopping.

2.1.2 Compatibility (CO)

Recently, mobile technology has become widely spread in people’s daily activities, such as activity like education, work, and even entertainment. The experiences and values of people’s lives will be creating through the diffusion of mobile technology (Lu & Su, 2009)

Rogers’ study on innovation diffusion theory (as cited in Shi, 2011) is widely used to explain the acceptance of technology for IT and IS research, especially study in mobile setting. "Diffusion of Innovation theory defines as the process by which an innovation is communicated through certain channels over time among the members of a social system" (as cited in Shi, 2011, p. 25). Eastin’s study (as cited in Wu & Wang, 2003) said that compatibility is one of the attribute of Diffusion of Innovation theory which is the degree to which an innovation is consistent with the existing values, past experiences and needs of potential adopters. The high compatibility idea will be less uncertain to the potential adopters and hence compatibility will positively influence on the rate of adoption of mobile shopping among mobile users (Sendeka, 2006).

Previous experiences like using internet shopping or e-shopping can be measured as compatibility indicator. Study by Cheong and Park (as cited in Aldás-Manzano, Ruiz-Mafé, & Sanz-Blas, 2003, p. 742) also indicate that internet experience positively affecting consumer’s confidence about mobile shopping. According to Yang’s study (as cited in Aldás-Manzano et al., 2003), the consumers’ experience of adoption of technologies related to mobile commerce positively affects mobile shopping perceptions.

Nowadays, the use of mobile technology is common in everywhere. Some people will feel insecure without mobile phone. A high degree of uniformity between one’s values and one’s usage experience with mobile technology will lead to the adoption of mobile shopping (Lu & Su, 2009). According to Aldás-Manzano et al., 2009, the features of time saving, convenience, variety and range of assortment and lower prices provided by mobile shopping are exactly similar with e-shopping. Those consumers with e-shopping experiences have a stronger intention to adopt mobile shopping than those who have never experiences.

H2: Compatibility has a positive significant relationship with M-Shopping adoption.

2.1.3 Social Influence (SI)

Mobile shopping is mushrooming over the world and acts as an emerging and important part of mobile commerce. Mobile shopping is expected to reach 31 billion by 2016 (Forrester, 2011). This is due to the expectation on consumer use of mobile shopping will be rapidly increasing and the increase of public in purchasing smart phone as what that is happening right now. The adoption of mobile shopping service is taking a vital role for both investor and service providers to gain profit from this innovation. There is one of the factors that takes role to influence perceive of public on mobile shopping, which is social influence.

Social influence factors deeply impact the user behaviour. Some theories show that social influence is important in shaping the behaviour of each customer. Social influence acts as a factor that will influence one’s behaviour (Shin, 2007). From social psychological and economic perspectives, there are two types of social influence that have been distinguished. The first will be mass media and second is interpersonal influence. Mass media comprises of the category of radio, television, newspapers, internet and so on. Mass media takes an important role to influence the mindset of public. According to innovation diffusion research, one’s adoption decisions are mainly influence by social network if as compared to one’s decision making way and also characteristic of IT (Hsu & Lu, 2004; Kim, Kim & Eun, 2009). One’s who faces media most of the time will easily get influenced by what the messages that been sent out by the medias. When medias are doing advertising on mobile shopping for particular company or website, it will indirectly influence the public and practicing on mobile shopping.

The second type that has been distinguished is interpersonal influence. Interpersonal influence is derived from social network such as peers, colleagues, friends and family members. It is defined as the communication that takes place between two persons who have an established relationship, where in another words, the people are in some way "connected" (Bruner & Tagiuri, 1954). Research shows that the attitude of customer based on the person’s behavioural intentions. Different customers will have different way of perceiving their behavioural intentions. It is defined as a kind of believe, which will significantly influence someone in any activity (Chong, Darmawan, Ooi & Lee, 2010). It also shows that different behavioural intentions will influence the subjective norms of a single person.

From the research that been conducted by Kim et al. (2009), it shows that social influence has a positive relationship with behavioral intention in adopting mobile entertainment service, which is also part of mobile shopping. Family decision does play a crucial role in influencing the family member’s purchasing behaviour and decision (Jung & Kau, 2004). Peers and colleagues do also take part in affecting one’s concept in purchasing behaviour, including mobile shopping. Malaysia is a country which is a strong family based society and community cohesiveness has been powerfully prioritised over the individual rights in Malaysia (Sani, Yusof, Kasim & Omar, 2009). Thus, the interpersonal influence that falls under social influence would be important point to affect one’s in adopting mobile shopping. If one’s family, social group and community have already adopted to mobile shopping, it will easily influence the others in adopting it too.

H3: Social influence has a positive significant relationship with M-Shopping adoption.

2.1.4 Technology Acceptance Model (TAM)

The research model examining mobile shopping adoption is being supported by Technology Acceptance Model (TAM) that cover two perceptions, perceived usefulness (PU) and perceived ease of use (PEOU). TAM presence is derived in the Theory of Reasoned Action (TRA) that discusses the users’ intention with the determinants of intended behavior (Fishbein and Ajzens, 1975).

PU is being defined on how useful is the technology to which a person believes that using the technology enable them to enhance their job performance (Davis, 1989). A positive feeling towards mobile shopping adoption can be generated when the consumers perceive M-shopping services are beneficial enough to assist their shopping (Davis, Bagozzi, Warshaw, 1992). In mobile setting, PEOU refers to the degree where effort is not needed when using an innovative technology (Davis, 1989). User’s intention to adopt technology innovation either directly or indirectly have the influence of PEOU towards consumers (Davis, 1989; Taylor and Todd, 1995; Venkatesh, 1999; Venkatesh and Davis, 2000).

Prior studies of TAM have being applied in several consumer technology adoption studies such as M-Commerce (Zhang, Zhu and Liu, 2012), M-Learning (Sim, Tan, Ooi and Phusavat, 2012), M-Banking (Cheah, Teo, Sim, Oon and Tan, 2011) and broadband adoption (Sim, Tan, Ooi and Lee, 2011). Wong et al. (2012) also applied TAM to determine user adoption of M-Shopping and the result proved that TAM model (PU and PEOU) significantly affect customer to adopt M-Shopping. Since then, TAM model has been validated as a robust and powerful framework for understanding the user’s adoption of technology (Hu and Stoel, 2009).

H4: Perceived usefulness has a positive significant relationship with M-Shopping adoption.

H5: Perceived ease of use has a positive significant relationship with M-Shopping adoption

2.3 Conclusion

This chapter provides the clear picture of all variables and relationship between the independent and dependent variable. Besides, it also helps us to understand how these independent variables (Innovativeness, Compatibility, Social Influence, Perceived Usefulness and Perceived Ease of Use) can influence the consumers in adopting M-Shopping.

CHAPTER 3: RESEARCH METHODOLODY

3.0 Introduction

Introduction presents the overview of the research methodology and carries out how the research is being study. Furthermore, those methods that will present in this chapter which include research design, data collection methods, primary data, secondary data, sampling design, target population, sampling technique, sampling size, research instrument, constructs measurement, data processing, data analysis and finally conclusion. For this research methodology, we will discuss about the research design, data collection methods, sampling design, operational definitions of constructs, measurement scales and methods of data analysis.

3.1 Research Design

This research is basically focuses on quantitative research. Quantitative research eventually generates numerical data that can be converted into statistical review or numerical form of analysis (Coville, 2011).

Quantitative data is mostly collected through questionnaire or even survey (Garwood, 2006). Questions that being measured by scales are being used to measure the consumer’s satisfaction, feeling or any other essential determinants. Therefore, quantitative research is often used to obtain a statistically review of respondents.

There are 3 research designs being used by researchers in exploring the intention to adopt information system, namely descriptive, exploratory and causal research. Descriptive research is used to provide wider information by describing and provide information about phenomenon systematically (Isikili, 1992). Exploratory research is being used to examine the general nature of the problem and having solutions for the problem. Lastly, causal research is where the researchers need to show whether one variable causes changes in value of another variable. In this study, descriptive and causal researchers are being involved. Descriptive research design is used to describe factors that might be influencing the intention to adopt mobile shopping in Malaysia and causal research design is used since there is relationship between all the independent variables (IN, CO, SI, PU and PEOU) and dependent variable (Intention to Use).

3.2 Data Collection Methods

3.2.1 Primary Data

Primary data is the data collection where a researcher has limited supporting data in secondary sources (Duval, 2005). In the process of conducting this research, a questionnaire survey was used to collect the primary data.

Questionnaire is a set of questions given to the selected respondents to collect data and information that could help in this research. Questionnaire is also known as self-administered survey where the researchers compile the answers from the respondents in order to know how the respondents behave in this research.

3.2.2 Secondary Data

Secondary data research project involves the gathering of existing journals or data that other researchers had originally collected through past research. Literature review, industrial surveys, statistic data and others are those examples of secondary data (Forshaw, 2000).

Published literature such as journals, magazines, and books are useful for researchers. Journals provide reliable data, theories, empirical studies that could be used as a guideline for this study. Magazines that related to this topic can be references to researchers to gather secondary data in their research.

Unpublished literature for example, World Wide Web (www) is a new source of secondary data that consist of wide range of information. This new media is less costly and time-saving where researchers can easily surf on it.

3.3 Sampling Design

3.3.1 Target Population

In order to further understand the antecedents that lead to the intention to adopt M-Shopping, a study was conducted. The respondent for this study is students that currently attached in University Tunku Abdul Rahman (UTAR), Kampar. This study decides to adopt university students as a sample due to several reasons. Firstly, young consumers are more likely into this technology compared to older consumers (Schadler, 2006). Secondly, younger consumers have better positive attitude on internet experience and even online shopping (Farag, Schwanen, Dijst and Faber, 2007). Thirdly, according to Burns (2005), college students are being labeled as heavy users of mobile phones. As a result, university students can be the representative for this study.

3.3.2 Sampling Technique

For this study, the researchers applied non-probability sampling to generate result. Non-probability sampling can represents a group of sampling techniques that basically assist researchers to select units from a number of populations.

The procedure of probability sampling that researchers used is convenience sampling. This is because convenience sample is the easiest to access where the units that being selected is simple. The researchers can achieve the targeted sample size in a relatively fast and cost saving method.

3.3.3 Sampling Size

The target sample size for the survey was actually aimed for full respond from UTAR, Kampar students but predicted that the rate of reply will not be as what researchers expected. The targeted sample for this research was aimed at 180 respondents.

3.4 Survey Instruments

The questionnaire is designed based on five variables, namely Innovativeness, Compatibility, Social Influence, Perceived Usefulness and Perceived Ease of Use. The independent variables were adopted from previous researchers that had done study on this topic as tabulated in Table 1.

The questionnaire was categorized into two sections, Section A and B. Section A contains eight general information questions whereas in Section B, Five-point Likert scale being used with ‘1’ as strongly disagree to ‘5’ as strongly agree which consists of four to six questions in each variables.

Prior to the survey, there must ensure the questionnaire quality and reliability before distribute the 180 sets of questionnaire survey to respondents. Therefore, there are 30 sets of questionnaire will be distributed as pilot studies to ensure that we can gain the reliable information and amendments the unreliable questions by running this pilot test.

Table 3.0: Cronbach’s Alpha (Reliability Test)

Independent Variables

Cronbach’s Alpha

No.of Items

Perceived Usefulness

0.819

5

Perceived Ease of Use

0.758

5

Innovativeness

0.555

4

Compatibility

0.814

5

Social Influence

0.760

6

Dependent Variables

Cronbach’s Alpha

No.of Items

Intention to Use

0.840

4

Table 3.0 shows the result of the reliability test for pilot test. The Cronbach’s Alpha coefficient is ranging from 0 to 1. As indicated in Table 1.0, the Alpha values range from 0.555 to 0.840. In this research, the result shows acceptable level of reliability.

Table 3.1: Questionnaires Sources

Constructs

Number of Items

Sources

Innovativeness

4

Wong et al. (2012)

Compatibility

5

Wong et al. (2012), Aldaz-Manzano et al. (2009)

Social Influence

6

Wong et al. (2012)

Perceived Usefulness

5

Aldaz-Manzano et al. (2009), Wong et al. (2012)

Perceived Ease of Use

5

Aldaz-Manzano et al. (2009), Wong et al. (2012)

3.5 Construct Measurement

3.5.1 Scale Management

3.5.1.1 Nominal scale

A nominal scale is really a list of categories to which objects can be classified (Statistic, 2010). It also represents the most fundamental level of measurement and assigns a value to an object for identification or classification purpose. Nominal scale is used in this study to measure personal information such as gender as stated in the questionnaire.

3.5.1.2 Ordinal scale

Ordinal is a variable can be treated as ordinal when its values represent categories with the intrinsic ranking. Ordinal scale has the ability to determine the relationship of one object to other object. In the research, ordinal scale is mainly applied to measure age judgements from respondent (Malhotra, 2012).

3.5.1.3 Interval Scale

An interval scale is a measurement scale in which a certain distance along the scale means the same thing no matter where on the scale you are, but where "0" on the scale does not represent the absence of the thing being measured.

The questionnaires were divided into two sections. In Section A was to collect respondent personal information such as gender, age, types of products, and so on. In Section B was to measures five independent variables and a dependent variable. Personal information is put in Section A instead of Section B because it’s may influence the choice of answering the questions.

Interval of scale is been using in Section B to measure the independent variables and dependent variable. Set it in five-points of likert scales ranging from strongly disagree to strongly agree and the respondents have to choose either one as an answer to reflect their opinion. This five-point of likert scales were to gain more reliable results in this research.

3.5.2 Operational Definitions

Table 3.2: Operational Definitions

Variables

Questions

Innovativeness

1. I think I would be the first in my circle of friends to

know where I can shop using a mobile phone

2. I think I would be the first in my circle of friends to

shop using a mobile phone

3. I think I know more about M-shopping than my

circle of friends

4. I think I would shop using a mobile phone even if I

did not know anyone who had done it before

Compatibility

1. Using M-shopping would be compatible with all

aspects of my life and work

2. I think that using M-shopping would fit well with the

current lifestyle that I like now

3. Using an advanced mobile phone to complete an

online transaction in a shorter time if I had used a

similar system before

4. Having internet shopping experience have a stronger

intention to engage in M-shopping

5. I think that using M-shopping fits well with the way I

like to do shopping

Social Influence

1. Suggestion and recommendation from friends will

affect my decision to use M-shopping

2. Family/relatives have influence on my decision to

use M-shopping

3. I will use M-shopping if my colleagues use it

4. Mass media (e.g., TV, newspaper, magazines,

internet) will influence me to use M-shopping

5. I will use M-shopping if the service is widely used by

people in my community

6. M-shopping will enable me to improve my social

status

Perceived Usefulness

1. Using M-shopping will enable me to accomplish

shopping tasks faster

2. Using M-shopping increases my productivity of my

shopping tasks

3. Effectiveness of my purchases would be further

enhance by using M-shopping

4. A better purchasing decisions can be made by using

M-shopping

5. Overall, I would find M-shopping to be beneficial

Perceived Ease of Use

1. I think that I could become skilful at M-shopping

2. Using M-shopping does not require a lot of mental

effort

3. I find it is easy to use mobile phone to find services

that I want

4. I think that I have the ability to shop using a mobile

phone without the help of others.

5. I think that I would find it easy to learn how to shop

using a mobile phone

Intention to use

1. I would use M-shopping for my shopping

convenience

2. Assuming that I have access to the M-shopping, I

choose to use it

3. In future, I intend to increase the usage of M-

shopping services

4. My interest towards M-shopping will increase in the future

3.6 Data Processing

3.6.1 Questionnaire Checking

Checking questionnaire is essential to prevent any inaccurate result that could lead to inaccurate conclusion. Questionnaire checking is basically excluded the questionnaires that are not complete, missing pages and also where the respondents do not follow the instruction given when attempted the questionnaire.

3.6.2 Editing

Editing the questionnaire can further improve its accuracy. Moreover, editing the data can improve the usefulness and also the accuracy of the research when detected the errors in the questionnaire. When the answer is ambiguous, researchers usually remove those unsatisfied response.

3.6.3 Coding

Coding is important before entering the data to facilitate us with fewer errors. Coding those variables such as gender, age, academic qualification and others are part of the variables that being measured. Example, 1 is for female and 2 for male.

3.6.4 Transcribing

After the editing data and coding of data are done, proceedings with data transcribing in order to make the data more assessable. The data collecting from questionnaire and the data coding is then being analyze by SPSS.

3.6.5 Cleaning

Data cleaning means continuously check the data to ensure the data is consistence and deal with missing response. SPSS software helps in checking the consistency of the data and values for all the variables.

3.7 Data Analysis Technique

Researchers are using the Software Package for Social Sciences (SPSS) 16.0 to analyze the data that had been collected. SPSS is designed to be a relatively comprehensive data analysis package for use in research. There are many types of analysis method. For example, descriptive statistics, Reliability Analysis, Pearson Correlation Coefficients Analysis and Multiple Regression Analysis are being used in this study. 180 questionnaires were distributed to respondents, and 142 completed questionnaires were collected and usable. The percentage of response rate is 78.89 %.

3.7.1 Descriptive Statistics

Descriptive Statistics is the numerical value that represents the total number of observations for a variable under a study (Hussey and Hussey, 1997). This statistics is aimed in Section A of the questionnaires to analyze the frequency of the demographic of the sample.

3.7.2 Reliability Analysis

Reliability is a necessary contributor to validity but it is not a sufficient condition for validity (Cooper and Schindler, 2006). The reliability analysis helped the researcher computed the Cronbach’s Alpha, where the reliability closer to 1.0, the better it is. The range less than 0.6 are considered poor whereas in range of 0.7 was considering acceptable and over 0.8 were considering good.

Table 3.2 Alpha coefficient Size

Alpha Coefficient Range

Strength of Association

< 06

Poor

0.6 to < 0.7

Moderate

0.7 to < 0.8

Good

0.8 to < 0.9

Very Good

≥ 0.9

Excellent

Source: Rules of thumb about Cronbach – Alpha Coefficient Size (Hair, 2007)

3.7.3 Pearson Correlation Coefficients Analysis

This analysis is appropriate for interval and ratio-scaled variables. The significant level in this analysis is 0.05 which means the confidence level is 95%. If the value p<0.05, the hypotheses are being supported. However, if the p>0.05, the hypotheses of this study are not being supported and it show that there is no relationship.

3.7.4 Multiple Regression Analysis

Multiple regression analysis is a flexible method of data analysis that may be appropriate whenever a dependent variable is to be examined in a relationship to independent variables (Cohen, Cohen, West and Aiken, 2003). Besides, it is defined as simultaneously develops a mathematical relationship between two or more independent variables and a dependent variable (Malhotra and Peterson, 2006). . The following is the general formula of multiple regression analysis:-

Ŷ=α+ βX1+ βX2+ βX3 + βX4, βX5

Whereby, Ŷ= dependent variable

α=fix and constant

β= coefficients of each independent variables

X1= Innovativeness

X2= Compatibility

X3= Social Influence

X4= Perceived Usefulness

X5= Perceived Ease of Use

3.8 Conclusion

In conclusion, our primary data and secondary data are used as our basic research methods to collect the relevant information. In addition, sampling design was also being done. So, after get the sample from population, then distribute the questionnaires to respondents. All data that we collect above will be useful for us in order to continue for the design instrument in the next part of our study.

CHAPTER 4: DATA ANALYSIS

4.0 Introduction

In this chapter, the data output are being calculated using SPSS 16.0. The 142 usable data was being analyzed by using Descriptive Analysis, Cronbach’s Alpha Pearson’s Correlations Analysis and Multiple Regression Analysis. Apart from that, this research also include the structure of respondents’ gender, age, types of phones that respondents using and their educational level.

4.1 Descriptive Analysis

4.1.1 Respondent Demographic Profile

4.1.1.1 Gender

Table 4.1: Frequency of Gender

Gender

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Female

64

45.1

45.1

45.1

Male

78

54.9

54.9

100.0

Total

142

100.0

100.0

Source: Developed for this research

Figure 4.1: Gender

Source: Developed for this research

Question 1 explored the gender of the respondent. As shown in the figure 4.1 and table 4.1, male is majority of the respondent which contains (78)54.93% while the amount of the female respondent is 64(45.07%) for this survey. There are total 142 respondents in this survey.

4.1.1.2 Age

Table 4.2: Frequency of Age

Age

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Below 20 Years Old

51

35.9

35.9

35.9

21-25 Years Old

84

59.2

59.2

95.1

26-30 Years Old

7

4.9

4.9

100.0

Total

142

100.0

100.0

Source: Developed for this research

Figure 4.2 : Age

Source: Developed for this research

Question 2 explored the age of the respondent. As shown in the figure 4.2 and table 4.2, the highest percentage of respondent is the age group of 21-25 years old which is 84(59.15%). Besides that, the second highest percentages of the respondent is the age group of below 20 years old which is 51(35.92%) whereas the third highest percentage of the respondent is age group of 26-30 years old which is 7(4.93%). There is no percentage for the age group of 31-35 years old.

4.1.1.3 Types of Products

Table 4.3: Frequency of Types of Products

Types of Products

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Mobile Phone

50

35.2

35.2

35.2

Personal digital assistant (PDA)

15

10.6

10.6

45.8

Smart Phone

77

54.2

54.2

100.0

Total

142

100.0

100.0

Source: Developed for this research

Figure 4.3 : Types of Products

Source: Developed for this research

Types of products using by respondents is discovered in question 3 of the questionnaire. Based on the figure 4.3 and table 4.3, majority of the respondents are using smart phone which is 77(54.23%) and followed by second highest percentages of products using is mobile phone which is 50(35.21%). The lowest percentage of respondents using personal digital assistant is 15(10.56%).

4.1.1.4 Academic Qualification

Table 4.4: Frequency of Academic Qualification

Academic Qualification

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Diploma/Advanced Diploma

21

14.8

14.8

14.8

Bachelor Degree/Professional Qualification

121

85.2

85.2

100.0

Total

142

100.0

100.0

Source: Developed for this research

Figure 4.4: Academic Qualification

Source: Developed for this research

Academic qualification of the respondents is discovered in question 4 in this questionnaire. Table 4.4 and figure 4.4 have shown that majority of the respondents holding Bachelor of Degree is 121(85.21%). Lastly, 21(14.79%) of the respondents’ education level is diploma or advanced diploma.

4.1.2 Central Tendencies Measurement of Constructs

4.1.2.1 Innovativeness

Table 4.5: Central Tendencies Measurement of Constructs: Innovativeness

Item

Mean

Ranking

IN 1

2.7254

4

IN 2

2.7817

3

IN 3

3.0000

2

IN 4

3.7465

1

Source: Developed for the research

The mean score of IN 4 with the statement of "I Think I would shop using a mobile phone even if I did not know anyone who had done it" is the highest among the four items which is 3.7465.

The last ranking is IN 1 with the statement of "I think I would be the first in my circle of friends to know where I can shop using a mobile phone" and the mean is 2.7254.

4.1.2.2 Compatibility

Table 4.6: Central Tendencies Measurement of Constructs: Compatibility

Item

Mean

Ranking

CO 1

3.6831

5

CO 2

3.7465

4

CO 3

4.0493

1

CO 4

4.0141

2

CO 5

3.9507

3

Source: Developed for the research

The mean score of CO 3 scored the highest mean value with 4.0493 with the statement of "Using an advanced mobile phone to complete an online transaction in a shorter time if I had used a similar system before".

CO 1 has the lowest mean score of 3.6831 with the statement of "Using M-shopping would be compatible with all aspects of my life and work".

4.1.2.3 Social Influence

Table 4.7: Central Tendencies Measurement of Constructs: Social Influence

Item

Mean

Ranking

SI 1

3.2465

5

SI 2

3.2183

6

SI 3

3.3662

4

SI 4

3.6831

2

SI 5

3.8521

1

SI 6

3.6549

3

Source: Developed for the research

The mean score of 3.8521 with the Item SI 5 has the highest mean value with the statement "I will use M-shopping if the service is widely used by people in my community".

Lastly, SI 2 with the statement of "Family/relatives have influence on my decision to use M-shopping" comes last with the score 3.2183.

4.1.2.4 Perceived Usefulness

Table 4.8: Central Tendencies Measurement of Constructs: Perceived Usefulness

Item

Mean

Ranking

PU 1

3.4085

5

PU 2

3.4648

4

PU 3

3.6338

3

PU 4

3.6268

2

PU 5

3.6972

1

Source: Developed for the research

The mean score of PU 5 with the statement of "Overall, I would find M-Shopping to be beneficial" is the highest among the five items which is 3.6972.

PU 1 has the lowest mean score of 3.4085 with the statement "Using M-shopping will enable me to accomplish shopping tasks faster".

4.1.2.5 Perceived Ease of Use

Table 4.9: Central Tendencies Measurement of Constructs: Perceived Ease of Use

Item

Mean

Ranking

PEOU 1

3.7183

5

PEOU 2

3.9366

2

PEOU 3

3.9085

4

PEOU 4

3.9366

2

PEOU 5

4.0070

1

Source: Developed for the research

PEOU 5 has the highest mean value with the statement "I think I would find it easy to learn how to shop using a mobile phone" compared with the rest of the five items.

Lastly, PEOU 1 has the lowest mean value with the statement of "I think that I could become skilful at M-shopping" with the reading of 3.7183.

4.1.2.5 Intention to Use

Table 4.10: Central Tendencies Measurement of Constructs: Intention to Use

Item

Mean

Ranking

IU 1

3.9225

3

IU 2

3.8732

4

IU 3

3.9296

2

IU 4

4.0845

1

Source: Developed for the research

IU 4 stated the highest value of mean with the statement of "My interest towards M-shopping will increase in the future" with the value of 4.0845.

IU 2 has the lowest mean value of 3.8732 among the four items with the statement of "Assuming that I have access to the M-shopping, I choose to use it".

4.2 Scale Measurement

4.2.1 Internal Reliability Test

Table 4.11: Reliability Statistics of Dependent and Independent Variables

Independent Variables

Cronbach’s Alpha

No. of Items

Perceived Usefulness

0.824

5

Perceived Ease of Use

0.777

5

Innovativeness

0.578

4

Compatibility

0.761

5

Social Influence

0.523

6

Dependent Variables

Cronbach’s Alpha

No. of Items

Intention to Use

0.778

4

The internal reliability level of 29 items in the questionnaire for the six variables which are PU, PEOU, IN, CO, SI and intention to use was studied by using the Cronbach’s alpha. "Cronbach’s alpha is a reliability coefficient that indicates how well the items in a set are positively correlated to one another" (Sekaran, 2003, p.307).

According to Table 4.11, PU has the highest value among all the 6 items which is 0.824, followed by intention to use with Cronbach’s alpha of 0.778, and it was measured using 4 items. PEOU which is measured in 5 items has the third-highest Cronbach’s alpha, 0.777, followed by CO which consists of 5 items with the Cronbach’s alpha of 0.761 and then the IN which comprised of 4 items with the Cronbach’s alpha of 0.578. SI has the lowest Cronbach’s alpha of 0.523, which measured in 6 items.

In general, the Cronbach’s alpha of the six variables, PU, PEOU, IN, CO, SI and intention to use fall between ranges of 0.523 to 0.824, Therefore, the result is reliable.

4.3 Inferential Analysis

4.3.1 Pearson’s Correlation

Table 4.12: Pearson’s Correlations Analysis

Correlations

Innovativeness

Compatibility

Social Influence

Perceived Usefulness

Perceived Ease of Use

Intention to Use

Innovativeness

Pearson Correlation

1

.387**

.078

.123

.358**

.305**

Sig. (2-tailed)

.000

.356

.144

.000

.000

N

142

142

142

142

142

142

Compatibility

Pearson Correlation

.387**

1

.261**

.421**

.622**

.612**

Sig. (2-tailed)

.000

.002

.000

.000

.000

N

142

142

142

142

142

142

Social Influence

Pearson Correlation

.078

.261**

1

.187*

.208*

.298**

Sig. (2-tailed)

.356

.002

.026

.013

.000

N

142

142

142

142

142

142

Perceived Usefulness

Pearson Correlation

.123

.421**

.187*

1

.480**

.306**

Sig. (2-tailed)

.144

.000

.026

.000

.000

N

142

142

142

142

142

142

Perceived Ease of Use

Pearson Correlation

.358**

.622**

.208*

.480**

1

.540**

Sig. (2-tailed)

.000

.000

.013

.000

.000

N

142

142

142

142

142

142

Intention to Use

Pearson Correlation

.305**

.612**

.298**

.306**

.540**

1

Sig. (2-tailed)

.000

.000

.000

.000

.000

N

142

142

142

142

142

142

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

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

Source: Developed for this research

According to Table 4.12, the p-value of IN, CO, SI, PU, PEOU and Intention to use are 0.000 respectively. All of the independent variable has positive correlation with Intention to use. The result shows that the entire variable are at the significant level of p<0.01. The strongest positive association with intention to use (r = 0.612 and r = 0.540) are CO and PEOU. Then it would be followed by the positive association between IN and intention to use(r = 0.305) and PU and intention to use(r = 0.306). Lastly, PU has the least positive association with intention to use

(r = 0.298).

4.3.2 Multiple Regression Analysis

Table 4.13: Multiple Regression Analysis

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

1.539

1.667

.923

.358

Innovativeness

.054

.078

.050

.699

.486

Compatibility

.368

.078

.410

4.705

.000

Social Influence

.136

.065

.140

2.080

.039

Perceived Usefulness

-.012

.054

-.017

-.228

.820

Perceived Ease of Use

.196

.070

.246

2.793

.006

a. Dependent Variable: Intention to Use

Source: Developed for this research

In the Table 4.13, it explained that the independent variables, CO, SI, and PEOU are significant correlated with the intention to adopt mobile shopping with the p value 0.000, 0.039, 0.006 and the p values are not more than 0.05. On the other hand, IN and PU are insignificantly correlated with the intention to use mobile shopping with p values of 0.486 and 0.820 and the p values are greater than 0.05.

According to Table 4.13, Innovativeness (IN) has B-value = 0.054 which indicate the higher the IN of the respondents the higher intention to adopt mobile shopping in Malaysia. Moreover, the B-value indicated that 1 unit increase IN can be accounted for a +5.4% changes in intention to adopt mobile shopping in Malaysia. Lastly, IN is also found to be not significant where the significant value is p = 0.486 (p > 0.05).

For Compatibility (CO) has B-value = 0.368 which indicate the higher the CO of the respondents the higher intention to adopt mobile shopping in Malaysia. Moreover, the B-value indicated that 1 unit increase CO can be accounted for a +36.8% changes in intention to adopt mobile shopping in Malaysia. CO is found to be significant where the significant value is p = 0.000 (p < 0.05).

Social Influence (SI) has B-value = 0.136 which indicate the higher the SI of the respondents the higher intention to adopt mobile shopping in Malaysia. 1 unit increase for SI in B-value can be accounted for +13.6% changes in intention to adopt mobile shopping in Malaysia. SI is also found to be significant where the significant value is p = 0.039 (p < 0.05).

The B-value of PU is -0.012 which shows the lower the PU of the respondents, the higher intention to use mobile shopping in Malaysia. The B-value indicated that 1 unit increase PU can be accounted for -1.2% changes in intention to use mobile shopping in Malaysia. PU is found to be not significant where the significant value is p = 0.820 (p > 0.05).

PEOU has a B-value of 0.196 which indicates the direction is positive which means the PEUO has positively significant effect on intention to practice mobile shopping in Malaysia. In addition, an increase of 1 unit in PEUO will lead to an increase of +19.6% of intention to practice mobile shopping in Malaysia. PEOU is found to be significant with the p value = 0.006 (p < 0.05)

Table 4.14: ANOVA

ANOVAb

Model

Sum of Squares

Df

Mean Square

F

Sig.

1

Regression

410.024

5

82.005

20.970

.000a

Residual

531.842

136

3.911

Total

941.866

141

a. Predictors: (Constant), Perceived Ease of Use, Social Influence, Innovativeness, Perceived Usefulness, Compatibility

b. Dependent Variable: Intention to Use

Source: Developed for this research

In Table 4.14, ANOVA test show that F value equals to 20.970 is significantly at 0.05 levels. The predictors of PEOU, SI, IN, PU and CO in the overall regression model have done well in explaining the variation in intention to use.

4.3.2.1 Test of Significance

H1: There is a positive significant relationship between IN and intention to adopt mobile shopping among mobile users in Malaysia.

Based on Table 4.13, IN has insignificant correlated with intention to adopt mobile shopping with the p-value of 0.486 and is greater than 0.05 for the p-value. Therefore, H1 is not supported.

H2: There is a positive significant relationship between CO and intention to adopt mobile shopping among mobile users in Malaysia.

According to Table 4.13, CO has a significant and positive relationship with intention to adopt mobile shopping with the significant level of 0.000. Thus, H2 is supported.

H3: There is positive significant relationship between SI and intention to adopt mobile shopping among mobile users in Malaysia.

Referring to Table 4.13, SI has a significant and positive relationship with intention to adopt mobile shopping with the significant level of 0.039. Thus, H3 is supported.

H4: There is a positive significant relationship between PU and the intention to adopt the mobile shopping among mobile users in Malaysia.

Based on Table 4.13, PU is insignificant with the p-value of 0.820 which is greater than p-value 0.05. In other word, PU has insignificant correlated with intention to adopt mobile shopping with significant level of 0.820. Thus, H4 is not supported.

H5: There is a positive significant relationship between PEOU and intention to adopt the mobile shopping among mobile users in Malaysia.

According to Table 4.13, PEOU has a significant and positive relationship with intention to adopt mobile shopping with significant level of 0.006. Thus, H5 is supported.

4.3.2.1 Strength of Relationship

.Table 4.15: Model Summary

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.660a

.435

.415

1.97752

a. Predictors: (Constant), Perceived Ease of Use, Social Influence, Innovativeness, Perceived Usefulness, Compatibility

Table 4.15 shows the model summary of multiple regression analysis. From the table, R square equal to 0.435 which means 43.5% of change in dependent variable (Intention to Use) can be explained by all independent variables (IN, CO, SI, PU and PEOU), the remaining 56.5% come from the variable which is not examined in this research.

4.4 Conclusion

In conclusion, the analyses of the data are being divided into descriptive analysis, scale measurement and inferential analysis. For the analysis of the data, we used the SPSS 16.0 which is the latest version available. In conclusion all of our IVs and DV are found to have positive significant relationship. The discussion of major findings, limitations of the study and recommendation for the future studies will be discussed in the following chapter.

CHAPTER 5: DISCUSSION, CONCLUSION AND IMPLICATIONS

5.0 Introduction

Chapter five will address the summary of statistical analysis which includes the summary of descriptive analysis and inferential analysis. Furthermore, followed by the discussion of major finding which discusses about the relationship between five independent variables and one dependent variable. Besides, the implication of study, limitation of research and recommendation for future research and conclusion of this research will be discusses in the chapter as well.

5.1 Summary of Statistical Analysis

5.1.1 Descriptive Analysis

5.1.1.1 Respondent Demographic Profile

From demographic information, there are 78(54.93%) males and 64(45.07%) females from overall sample of 142 respondents. Among the respondents, the highest percentage of respondents is the age group of 21-25 years old which is 84(59.15%) following by second highest percentages of the respondents is the age group below 20 years old which is 51(35.92%). Besides that, the third highest percentage of the respondents is age group of 26-30 years old which is 7 respondents or 4.93%. Lastly, there is no percentage for respondents in the age group of 31-35.

For the types of products using by respondents, 77 respondents or 54.23% are using smart phone. For the mobile phone, 50 out of 142 respondents or 35.21% are using it whereas the remaining 15 out 142 respondents or 10.56% are using personal digital assistant.

For the academic qualification of overall 142 respondents, the highest is 85.21% or 121 respondents are degree holders. The lowest group is the group of respondents’ education level is diploma or advanced diploma, which consist 14.79% or 21 respondents.

5.1.1.2 Central Tendencies of Constructs

For the construct of IN, the statement "I think I would shop using a mobile phone even if I did not know anyone who had done it" has the highest mean reading of 3.7465 whereas the statement of "I think I would be the first in my circle of friends to know where I can shop using a mobile phone" has the lowest mean of 2.7254.

For the construct of CO, the highest ranking mean fall to the statement of "Using an advanced mobile phone to complete an online transaction in a shorter time if I had used a similar system before" with the mean of 4.0493 and the statement of "Using M-shopping would be compatible with all aspects of my life and work" fall under the lowest mean value of 3.6831.

For the construct of SI, the statement "I will use M-shopping if the service is widely used by people in my community" has the highest mean reading on 3.8521 whereas the statement of "Family/relat