Smartphone Usage Among Students

CHAPTER 1: INTRODUCTION 1. Introduction: Smartphone Usage Mobile phones nowadays are addressed as smartphone as they offer more advanced connectivity and computing ability than a normal cell phone. The term smartphone refers to a programmable mobile phone that offers advanced capabilities and features that help individuals in their daily work and personal life (Euromonitor, 2010). Smartphone basically is the combination of both cell phone and a PDA. 70% of the world’s population own at least one mobile phone. In a telephone survey, 83% of respondents said that they owned a cell phone and 35% of the 2,277 U. S. dults said that they owned a smartphone. Literately, a smartphone is a handheld computer, as it is powerful enough to deliver various functionalities comparable to a computer. The release of dual-core processors smartphone recently has further reaffirmed this assertion. A research on 5013 US adult smartphone Internet users at the end of 2010 reveal the types of smartphone users. i. General Smartphone Usage: Cell phones have been a must have item in daily lives. With the invention of smartphones, owing a cell phone is no longer for calling; it has become a trend and is a substitute for computers, telephone and PDA. 1% uses smartphone to browse the Internet, 77% search, 68% use an application and 48% watch videos on their smartphone. ii. Action-Oriented Searchers: Smartphones is used to find wide variety of information and to navigate the mobile internet. Search engine websites are the most visited websites with 77% of US smartphone users citing this. iii. Local Information Seekers: Smartphone is convenient because it users can easily access to information through internet and software provided. 95% of US smartphone users have looked for local information. iv.

Purchase-driven Shoppers: Smartphones has been relatively useful for women because it provides shopping tools, from comparing prices, finding more product information to locating a retailer. 74% of US smartphone shoppers make a purchase, whether online, in-store, or on their phones. v. Reaching Mobile Consumers: Businesses never miss the opportunity to advertise their products. With smartphones, consumers are exposed cross-media and a majority of them notice mobile ads which lead to taking action on it. 82% notice mobile ads with half of take action, 35% visiting a website and 49% making a purchase.

Figure 1. 1 Smartphone Penetrations across Global Markets Source: http://www. asymco. com/2011/12/13/global-smartphone-penetration-below-10/ (2011) Smartphones have penetrated many countries since its first launching. The number of users started to expand massively in 2010. Figure 1. 1 depicts Singapore to be the country with the most smartphone penetration in year 2011. 2. Smartphone usage in Malaysia With the popularity and functions offered in the phone, smartphones have seen an increase in terms of demand (Park and Chen, 2007). It is reported that in year 2010, 85% of Malaysians own mobile phones.

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With mobile broadband becoming more widely available and affordable, it’s not surprising that a growing number of Malaysians are accessing the Internet via smartphones. Massive competition on mobile broadband industry causes the price of subscription become lower. This is an advantage to middle income people especially to students as they now have the ability to own a smartphone and utilise it with mobile internet. More than half of Malaysian consumers (55%) are using laptops and netbooks while eleven per cent said they are using smartphones which is a nine point gain from 2009.

Almost two in ten (19%) Malaysians aged 20-24 access the Internet via their mobile phones. Figure 1. 3: Mobile and Smartphone Sales in Malaysia Source: http://marketresearchbulletin. com/? p=3636 The data from the Figure 1. 3 shows that the number of smartphones sold doubles from 2009 to 2010. Since the beginning of 2010, value sales of smartphones have been consistently increasing every month and occupied 72 per cent of the overall pie by December. Overall, close to two in five (38 per cent) mobile phone sets sold last year were smartphones.

In Malaysia, it was found that smartphone sales totalled 172. 4 million units in year 2009, with a 23. 8 per cent increase from 2008 (Sidhu, 2010). This increment in sales was partly contributed by university students (Jacob and Isaac, 2008). 3. Research Problem Mobile phones have been more and more versatile and with smartphones, it makes communication convenient between and among individuals, especially students. Communication and life makes easy as smartphones provides Internet capability and functionalities that are similar to computers.

Students nowadays are prone to using Social networking services (SNS) to spread information. With smartphones, students can instantly share ideas, activities, news, and interests anytime and anywhere. The problem therefore is to understand whether attitudes will affect the intention towards using smartphone among students. Attitude is a feeling, beliefs or opinion towards something. Positive attitude can result in beneficial usage of smartphones by students such as to use it as a medium of learning.

On the others hand, negative attitude such as to abuse the use of smartphone will develop negative effects to the users such as incompetent and unable to meet deadlines and reduces the productivity which will affect the user overall daily routine. The next question that we want to research is on whether perceived behavioural control can influence the intention to use smartphones. Perceived behavioural control is an individual’s perceived ease or difficulty of performing the particular behaviour.

It is linked to control beliefs, which refers to beliefs about the presence of factors that may facilitate the behaviour. 4. Research Objectives Research objectives are the objective that we intend to achieve after identifying research problems. There are some of research objectives that are highlighted in this research. One of our main objectives of this research is to understand the determinants of attitude among students in using smartphones. We are going to find out the relationship of the key determinants such compatibility, perceived usefulness and perceived ease of use in influencing the attitude.

Secondly, the purpose of this research is to understand the factors that will influence the intention of students to use smartphones. Lastly, this study will also seek to understand the role of attitude on intension. 5. Research Questions In seeking to achieve the above objectives, this study attempts to answer the following research questions: 1) What are the key determinants of intention? 2) Does attitude moderate the relationship between perceived usefulness, perceived ease of use, compatibility, observability, trialability, self-efficacy and intention? ) Does perceived usefulness, perceived ease of use, compatibility, observability, trialability, self-efficacy influence intention to use? 6. Significance of Study The study is carried out to help us understand the key determinants of intention to use smartphones among students, using attitude as the moderator to the relationship. It helps us to have clearer picture on how the determinants will affect the intention of using smartphones among students by looking at the independent variables that are directly and indirectly affecting the dependent variable (actual use).

Understanding the determinants for intention to use will raise awareness regarding usefulness of smartphones to students and will create higher level of acceptance to smartphone in the future. This study will help to give insight on the grey areas of smartphones and enable us to understand better the social and psychological factors that may affect the intention to use smartphone among students. The results from this study can be used by mobile phone manufacturers to improve the functions and elements in smartphone which will attract new users especially students and continue to bring extra benefits to the present users.

In addition, this result can be used as a benchmark for smartphone manufacturers to be creative and innovative in developing new ideas that could help users especially students in learning process. Therefore, understanding the key factors that will increase the intention to use smartphone will result in better suitability in functions to students. 7. Definition of Key Terms Perceived Usefulness – defined as the degree to which a person believes that using a particular system would enhance his or her job performance. Davis, 1989) Perceived Ease of Use – defined as the degree to which a person believes that using a particular system would be free of effort. (Davis, 1989) Compatibility – defined as the degree to which using an innovation is perceived as consistent with the existing sociocultural values and beliefs, past and present experiences, and needs of potential adopters. (Rogers, 1983) Observability – defined as the degree to which the results of an innovation are visible to others. (Rogers, 2003) Trialability – defined as the degree to which an innovation may be experimented with on a limited basis. Rogers, 2003) Self-Efficacy – The judgments an individual makes about his or her capability to mobilize the motivation, cognitive resources and course of action needed to orchestrate future performance on a specific task. (Martocchio and Dulebohn, 1994) Attitude – A psychological tendency that is expressed by evaluating a particular entity with some degree of favour or disfavour” (Chaiken, 1993) Intention – the extent to which an individual intends to perform a specificbehavior. (Davis et al. ,1989). 8. Organization of the Report This research proposal is organized into five chapters.

Chapter 1 gives the background of the study. The purposes and research objectives have been put forth to guide the direction of the study. Chapter 2 reviews related literatures by previous researchers. Based on these literatures the theoretical framework and hypotheses are developed. Chapter 3 discusses the research methodology used in this research. Chapter 4 presents the result of the statistical analysis. Chapter 5 summarizes research findings, implications of the findings and limitation of the study. The concluding chapter also provides some suggestions for further studies. CHAPTER 2 LITERATURE REVIEW 2. Introduction This chapter focuses on discussing the theories, the expansion of the theories to the present theoretical framework used in this research and the justification for the present model. 2. 2 Overview of the literature Various literatures from scholars in Malaysia and abroad were reviewed on the subject Theory Acceptance Model (TAM) and Innovation-Diffusion Theory (IDT). Among numerous perspectives that can be used to examine user acceptance and usage behavior of new technologies, TAM might be the most popular one. This model is derived from Fishbein & Ajzen’s (1975) Theory of Reasoned Action.

Davis (1986) developed TAM specifically for explaining and predicting user acceptance of computer technology. The goal of TAM is “to provide an explanation of the determinants of computer acceptance that is in general, capable of explaining user behavior across a broad range of end-user computing technologies and user populations, while at the same time being both parsimonious and theoretically justified”. The Technology Acceptance Model posits the determinants of user acceptance that may be able to explain a user’s behavior in regard to a general user’s computing technologies.

The TAM claims that users evaluate the system based on the system’s ease of use (PEOU) and perceived usefulness (PU). If the system is easy to use and useful, a user would have a positive attitude toward the system (AT), which in turn causes a user’s actual intention to use (BI). Then, the intention creates a user’s decision to use the system. A previous study conducted by Park and Chen indicated that behavioral intention to use a smartphone was largely influenced by perceived usefulness and attitude toward using a smartphone.

They further postulated that perceived usefulness and perceived ease of use positively determine attitudes toward using a smartphone. Kwon & Zmud (1987) suggest that when discussing IDT-related subjects’ factors such as task, individual, organization, and environment as additional explanatory factors should be introduced. Task includes structure of the task, jurisdiction, and uncertainty. Individual factors include aspects such as education, age, experience, and personal specialties.

Organizational factors include the support of higher-level management, the organizational structure, the involvedness of the users, and the quality of the product. Environmental factors include pressure from competitors, customer satisfaction, and marketing strategies. The context of smartphone adoption contains both individual factors and organizational diffusion. Previous innovation diffusion studies have suggested that innovation attributes affect an individual’s attitude of the innovation prior to adoption and may consequently influence the speed of adoptions.

This study employed these attributes in building the theoretical basis for behavioral characteristics. These beliefs include, compatibility, trialability, self- efficacy and observability. 2. 3. Theory Acceptance Model (TAM) The TAM probably is the most popular theory explaining user acceptance and behavior related to new technologies. Davis (1989) developed the TAM and investigated the determinants of user acceptance that may explain a user’s behavior in regard to the user’s general attitude toward the use of computing technologies.

According to the TAM, users evaluate the system based on the perceived ease of use and perceived usefulness of the system. If the system is perceived as easy to use and useful, a user would have a positive attitude toward the system, which in turn leads to the user’s intention to use the system. Then, the intention results in the user’s actual decision to use the system. We are using the Technology Acceptance Model to test the perceived usefulness and perceived ease of use about the intention to use smart phones among students.

The Technology Acceptance Model (TAM) has become a well-established robust model for predicting user acceptance (Davis, 1989; Davis, Bagozzi, & Warsaw, 1989). TAM is one of the most influential extensions of Ajzen and Fishbein’s (1975) theory of reasoned action and specifies two key constructs that influence users’ attitudes, intentions, and behaviors related to technology adoption and use (Lippert & Forman, 2005). The parsimony of TAM combined with its predictive power makes it easy to apply to different situations. However, while parsimony is TAM’s strength, it is also the model’s key limitation.

TAM is predictive but its generality does not provide sufficient understanding from the standpoint of providing system designers with information necessary to create user acceptance for new systems (Mathieson,1991). TAM provides researchers with “valid, reliable, and easy to administer scales for the key constructs” (Venkatesh et al. , 2007, p. 268). Due to the reliability of these measurement scales, questions for the survey instrument in this study were adapted from this information. Venkatesh et al. noted the repeatability and validity of TAM.

TAM was confirmed to be generalizable over time in various research papers worldwide, testing numerous technologies, diverse settings, and different populations. Predicted validity was also confirmed by a number of research studies investigating intention, self-reported use, and actual use. Ramayah (2006a) and (Venkatesh, 2000) have added depth to TAM model by understanding the determinants of perceived ease of use in their study. The study by (Venkatesh, 2000) explained up to 60% of the variance in system specific perceived ease of use.

The study by (Ramayah, 2006a) on determinants of perceived ease of use of e-Library also explained up 65% of the total variance. These studies have some of the highest explanatory power among TAM research conducted in recent years. The TAM is a specific model developed to explain and predict user’s smartphone usage behavior. Derived from the TAM, it predicts user acceptance based on the influence of two use beliefs: Perceived Usefulness (PU) and Perceived Ease of Use (PEU). 2. 3. 1 Limitation of Theory Acceptance Model (TAM)

TAM may be criticized, however, for the lack of sufficient explanation about cognitive processes culminating in a user’s acceptance of new technology. TAM still shares the basic premises and components outlined in Ajzen and Fishbein’s Theory of Reasoned Action (Ajzen and Fishbein, 1980), but by excluding the attitude construct from the TRA model, TAM discounts the role of attitude in explaining technology acceptance behavior. Venkatesh and his colleagues dropped the construct of attitude from the technology acceptance model (Venkatesh and Davis, 1996; Venkatesh and Davis, 2000; Venkatesh et al. 2003), arguing that the role of attitude in explaining behavioral intention or actual adoption behavior is very limited and is at best a partial mediator in the relationship between salient beliefs and the adoption behavior or intention. We contend that this argument is made without serious theoretical consideration and restricts the search for a comprehensive understanding of technology acceptance. 2. 4 Innovation Diffusion Theory (IDT) The IDT describes the process of technology acceptance by five characteristics of the technology influencing the consumer’s attitude leading to adopting or refusing the technology (Rogers, 1995).

The main difference appears to be TAM’s focus on a specific technology whereas IDT recognize the importance of establishing a technology’s likelihood to be adopted in relation to comparable existing technologies (Park & Gretzel, 2006). Diffusion of Innovation Theory (DIT or DOI) (Roger 1995) is a well-known conceptual framework to study new products’ diffusion and adoption. The original diffusion model provided a probabilistic approach based on the hazard function, which determines the likelihood that an agent who has remained a non-adopter of an innovative product will become an adopter in the next temporal unit.

Rogers [1983] explained the process of innovation diffusion as one which is dictated by uncertainty reduction behaviour amongst potential adopters during the introduction of technological innovations. Even though innovations typically offer its adopters novel ways of tackling day-to-day problems, the uncertainty as to whether the new ways will be superior to existing ones presents a considerable obstacle to the adoption process. To counter this uncertainty, potential adopters are motivated to seek additional information, particularly from their workplace peers [Brancheau & Wetherbe, 1990].

In diffusion research theory (Rogers, 1995), diffusion is classified into five stages: innovators, early adopters, the early majority, the late majority, and laggards, with 2. 5%, 13. 5%, 34%, 34%, and 16% of the population respectively. These barriers are closely connected to all kinds of access-related issues, i. e. access to the physical device needed to use a new mobile service, i. e. the smartphone, or access to money to pay for the hardware to use the service, or to pay for the service itself.

Innovation Diffusion Theory (IDT) consists of six major components: innovation characteristics, individual user characteristics, adopter distribution over time, diffusion networks, innovativeness and adopter categories, and the individual adoption process [Tornatsky & Klein, 1982; Rogers, 1983; Brancheau & Wetherbe, 1990; Moore & Benbasat, 1991; Taylor & Todd, 1995(b)]. According to IDT, the rate of technology diffusion is affected by an innovation’s relative advantage, compatibility, trialability, observability and complexity.

Research suggests that all but the last factors have a positive influence on diffusion (Sonnenwald, Maglaughlin and Whitton 2004; Ferle, Edwards and Mizuno 2002). Rogers (1995) defines relative advantage as ‘the degree to which an innovation is seen as being superior to its predecessor’. The IDT posits an array of innovation characteristics that may impact a user’s perception of the innovation preceding adoption of the innovation. As a result, these characteristics presumably affect the speed of innovations being embraced. These attributes further provide a theoretically-based set of socio-behavioral beliefs.

Thus, we adopted IDT because of the innovative nature of smartphone devices. Innovation may be defined as a new use of an idea, practice, or object by the unit of adoption. This definition of innovation can be applied to new technology adoptions among students. Rogers defined innovation as a new use of an idea, a practice, or an object by the unit of adoption. The smartphone was introduced in 2000. Thus, we view smartphone devices as recent innovations and employ Rogers’s DOI theory in our study. Researchers have used the theory to better understand whether an individual or an organization will adopt new innovations. 2. Theoretical Framework Theoretical frameworks in quantitative research help to “provide a conceptual guide for choosing the concepts to be investigated, for suggesting research questions, and for framing the research findings” (Corbin & Strauss, 2008, p. 39). Figure 2. 5. 1 Theoretical Framework 6. Independent Variable 2. 6. 1 Perceived Usefulness In Technology Acceptance Model, behavior intention is influenced by both perceived usefulness and attitude. This relationship has been examined and supported by many prior studies (Adams et al. , 1992; Davis et al. , 1989; Hu et al. , 1999; Venkatesh and Davis, 1996, 2000).

Perceived usefulness refers to the degree to which a person believes that using a particular system would enhance his or her job performance, (Davis, 1989). Many earlier studies have shown that perceived usefulness was the major determinant of attitude towards system use (Langford and Reeves, 1998; Venkatesh and Davis, 1996). Empirical studies have shown that perceived usefulness has a strongly impact on usage than ease of use. Perceived usefulness are existing in the studies of technology to shown that perceived usefulness directly and significantly influences behavioral intention to use smartphone (Chen and Ching, 2002; Chen et al. 2002; Heijden et al. , 2003; Guriting and Ndubisi, 2006; Khalifa and Shen, 2008; Liao et al. , 2007; Lin and Wang, 2005; Luarn and Lin, 2005; Wei et al. , 2009; Lai and Yang, 2009). However, Davis et al. (1989) to suggest that perceived usefulness may impact on behavioral intention to use the technology-based system. H1: Perceived usefulness is positive related to intention to use. H2: Perceived usefulness is positive related to attitude. 2. Perceived Ease of Use Perceived ease of use refers to the extent to which an individual perceived that using a system is easy or effortless (Davis, 1989).

Earlier studies revealed that if an individual perceives a system to be easy to use, he/she is more likely to perceive the system to be useful also (Morris and Dillion, 1997). In addition, if an individual perceives the system to be easy to use, the individual is more likely to use the system, especially among novice users. In a test of selling, when consumers perceive that making a purchase from a virtual store is easy to understand and do, they usually continue interacting with that site (Barkhi and Wallace, 2007). However, by the prior literature by Davis et al. 1989) proposed that perceived ease of use is predicts attitude towards the channel, and also an antecedent of perceived usefulness. Technology acceptance model (TAM) (Davis et al. , 1989; Mathieson, 1991; Davis and Venkatesh, 1996; Gefen and Straub, 2000; Al-Gahtani, 2001) determined by perceived usefulness (PU) and perceived ease of use (PEOU) relating to the attitude toward use that relates to intention and finally to behavior but there is no direct related with actual use. H3: Perceived ease of use is positive related to intention to use H4: Perceived ease of use is positive related to attitude. 2. 6. 3 Compatibility

Compatibility (Park and Gretzel, 2006) is the degree to which in an innovation is perceived as being consistent with the existing values, needs, and past experiences of potential adopters. Compatibility (Gavin J. Putzer, 2010) has a positive effect on the rate of adoption. When a user recognizes that an innovation is compatible with a system, the more the innovation will be adopted. Compatibility (Rogers,1995) refers to ‘the degree to which an innovation is seen to be compatible with existing values, beliefs, experiences and needs of adopters’. In a conjoint analysis directed at the adoption of mobile games, Kleijnen et al. 2004) found that perceived risk, which are often used in extensions of Rogers’ concepts (Ortt, 1998) of complexity, and are also referred to as relative ease of use and compatibility, are important factors in the intention to use of mobile services(eg Smartphone) . According to Kleijnen et al. (2004), this implies that mobile systems (eg Smartphone) have to be reliable and data-transmission has to be secure, while the systems have to be easy to navigate and fit into the daily routine of users. H5: Compatibility is positive related to intention to use H6: Compatibility is positive related to attitude . 6. 4 Observability Observability (Park and Gretzel, 2006)is the degree to which the results of an innovation is observable to others. Observability (Yangil Park,2010) has a positive effect on adoption. When a user has an opportunity to observe an innovation, the innovation is more likely to be adopted. Observability(Rogers,1995) is the ‘degree to which the results of an innovation are visible’. An innovation factor from the Kwon and Zmud model known as trialability was removed from our model to reduce possible confusion with another innovation factor known as observability.

The final pair of characteristics, results demonstrability and visibility, are derived from Rogers’ observability characteristic. Result demonstrability is defined as the tangibility of the results of adopting an innovation, and visibility as the degree to which prospective users see an innovation as being visible in the adoption context [Moore & Benbasat, 1991; Agarwal & Prasad, 1997]. H7: Observability is positive related to intention to use H8: Observability is positive related to attitude 2. 6. 5 Trialability Trialability (Park and Gretzel , 2006) is the degree to which an innovation may be experimented with before an adoption.

Trialability (C Huang,2010) existence negative relationship with the attitude of use. Trialability (Rogers,1995) is the ‘degree to which an idea can be experimented with on a limited basis’. If a person can try out the technology before deciding to accept Smartphone, the person will develop a stronger attitudinal belief about the technology, either in a positive or in a negative way depending on the quality of the new technology (Karahanna et al. , 1999; Venkatesh & Brown, 2001; Xia & Lee, 2000; Choi et al. , 2002). Therefore, if a user as an opportunity for trial usage before enroll with Smartphone; the person will have positive attitudinal belief and intention to use Smartphone. H9: Trialability is positive related to intention to use H10: Trialability is positive related to attitude 2. 6. 6 Self Efficacy Self-efficacy (SE) refers to individuals’ belief in their ability to perform a specific task in a given situation or context (Bandura, 1977). Bandura (1977) states that efficacy expectations—the belief that one can perform an activity in question—are the major antecedent of activity choice and effort. Jengchung Chen, 2010) is recognized to be a more important than the others. Efficacy refers to the belief that an individual has the ability to perform a particular behavior. Compared with competing models, TAM is believed to be more accurate and parsimonious when it is used to predict technology adoption. However, the parsimony of TAM often results in the model being less informative in understanding usage behavior. Due to this limitation, researchers have attempted to extend the TAM framework by encompassing various constructs such as gender, culture, trust, experience, social influence, and self-efficacy.

Among those constructs, self-efficacy is recognized to be a more important than the others. Efficacy refers to the belief that an individual has the ability to perform a particular behavior. Self-efficacy has been documented in numerous studies to be an important determinant of PEOU. In the context of web technologies, Agrawal et al (2000) found a positive effect of self-efficacy on both PU and PEOU. Similarly, Ma & Liu (2005) found that self-efficacy positively influences PU, PEOU, and the intention to use smartphone. H11: Self Efficacy is positive related to intention to use. . 7 Mediating Variable 2. 7. 1 Attitude According to Antonides et al. , (1998), “Attitude is the individual predisposition to evaluate an object or an aspect of the world in a favorable or unfavorable manner. ” In Fishbein & Ajzen’s (1975) formulation, attitudes influence behaviour through behavioural intentions. Past studies indicate that the link between attitude toward the object and behaviour is not always clear. In some cases, attitudes have a direct effect on behaviours (Bagozzi & Warshaw 1992) but no effect in Bagozzi (1992).

Both PU and PEU are posited as having significant impact on a user’s attitude (AT) toward using smartphones. (Yong-Wee Sek 2010) Based on an analysis of four different types of mobile services, Nysveen et al. (2005b) conclude that, in all four cases, people’s intention to use mobile services as well as their attitude toward the actual use, is affected significantly by the direct motivational influence of enjoyment. Moore & Benbasat [1991:196] reminds us, however, that these definitions are, in fact, “based on perceptions of the innovation itself and not on the perceptions of actually using the system”.

As Fishbein & Ajzen [1980] concur, attitudes towards an object and attitudes regarding a particular behaviour relating to that object can frequently differ. Attitude towards behaviour can be described as an individual’s subjective forecast of how positive or negative he / she will feel when performing the target behaviour, whereas subjective norm can be viewed as an individual’s perception of the social pressure on him / her to perform the target behaviour [Fishbein & Ajzen, 1975; Ajzen & Fishbein, 1980].

Furthermore, according to the expectancy value model of attitude [Fishbein & Ajzen, 1975], an individual’s attitude towards performing the target behaviour is itself determined by his / her beliefs regarding the consequences of performing the target behaviour, as well as the evaluation of these consequences. Attitude is explained as a function of the combined effect of behavioural beliefs and outcomes evaluations [Mathieson, 1991]. The behavioural beliefs relate to the favourable utilitarian, hedonic and social outcomes that can result from performing the behaviour [Venkatesh & Brown, 2001]. Davis et al. 1989) indicated that the key purpose of TAM is to provide a basis to trace the impact of external factors on internal beliefs, attitudes and intentions. Many IT researchers have since used TAM as a basis to explore and identify other determinants and relationships specific to a particular IT usage in different contexts (Venkatesh et al. , 2003). Hence, since the intention of smart phone among students is very closely tied attitude, this theory should be directly applied to the adoption of this innovation. (Check-Yee Law 2010) H12: Attitude is positive related to intention to use 2. 8 Dependent Variable 2. 8. 1 Intention to use

Intentions are different form attitudes where attitudes are summary evaluations, intentions represent the person’s motivation in the sense of his or her conscious plan to exert effort to carry out a behavior (Eagly & Chaiken 1993). Behavioural Intentions (BI) to use is jointly determined by a person’s attitude toward using the system and its perceived usefulness (Shahril Bin Parumo 2010). Behavioural intention is a measure of the strength of one’s intention to perform a specified behaviour (Fishbein and Ajzen, 1975). It is correlated with the usage (Davis et al. , 1989) and is a predictor for usage (Szajna, 1996).

Purchase intentions are personal action tendencies relating to the product (Bagozzi et al. 1979). Intentions are different from attitudes where attitudes are summary evaluations, intentions represent the person’s motivation in the sense of his or her conscious plan to exert effort to carry out a behavior (Eagly & Chaiken 1993). At times, intention is also difficult to measure. For instance, Bagozzi, Baumgartner & Yi (1989) commented that when an individual is unclear about his or her intention in regards to some action, there is strong tendency for him to react based on their past actions.

Here, the individual is likely to report his or her habit rather than intention when responding to the intention (Warsaw & Davis, 1985). Despite issues, purchase intention is an important construct in consumer behavior (Kotler & Armstrong, 2003). A previous study conducted by Park and Chen indicated that behavioral intention to use a smartphone was largely influenced by perceived usefulness and attitude toward using a smartphone. The Theory Acceptance Model is the most popular intention-based theories and models that have emerged from this school of thought [Chau & Hu, 2002].

CHAPTER 3: METHODOLOGY 3. 1 Introduction The purpose of chapter 3, methodology is to explain the process or the steps taken to answer the research problems. The process may be expanded to include a philosophically coherent collection of theories, concepts or ideas as they relate to a particular discipline of inquiry in this research. Discussion in this chapter will consists of the research model, variables and measurement, population, sample and sampling techniques, data collection technique and techniques of analysis. 3. 2 Research Model 3. . 1 Type of Study This is correlational study. This study was conducted among students in Universiti Sains Malaysia who are personally using smartphones. Hypotheses testing was undertaken to explain the variance in the dependent variables to predict the relationship. We will begin by discussing the relationship that certain events might have to one another whether there is a positive correlation or negative correlation or no correlation. 3. 2. 2 Nature of Study This study was conducted under the non-contrived setting (natural environment).

The variables are neither controlled nor manipulated. This is a cross sectional study where data were collected within 2 weeks. Data is only collected from willing students from Universiti Sains Malaysia. 3. 2. 3 Unit of Analysis The unit of analysis is individual who are students using smartphones in USM. 3. 2. 4 Research Site The research sites for this study are individuals who study in USM, Penang. 3. 3 Population, Sample Size and Sampling Technique The population consists of individuals who are students of Universiti Sains Malaysia (main campus) that uses smartphone.

The general rule for the of analysis independent variable, sample size must be five-to-one ratio (5:1) of the independent variable, which means that number of respondent must be at least 30. However, based on Hair et al. (1988) he proposed that the acceptable ratio is ten-to-one (10:1) of the independent variable, which means in a research must have minimum 60 respondents. The sampling technique used is non-probability sampling method. Non-probability sampling method is used because only little attempt is made to generate a representative sample.

Besides, there is no need to generalize compared to probability sampling and feasibility. Moreover, when there come to limited objectives, non-probability will be a good choice. Judgment method has been chosen as the sampling technique for this study because there is a need to find out whether people that we approach have access to social networking sites before filling up the questionnaire. This ensures credibility of this research. The list of smartphone users among students in Penang cannot be obtained therefore probability sampling could not be done. . 4 Scale and Measurement The questionnaire was divided into 10 sections. Section 1 to 8 is measured using interval scale of measurement. The other two sections, personal profile and internet experience is measured by using nominal and ordinal scale. For section 1 to 8, the respondents were asked to read and respond to all questions according to their level of agreement or disagreement using the 5 point scale. The ratings are as below: 1 Strongly Disagree 2 Disagree 3 Neutral 4 Agree 5 Strongly Agree

All instruments were adopted from various literatures and were modified for the purpose of understanding people’s reflection when they use smartphones. 3. 4. 1 Independent Variable The independent variable is defined as the presumed cause of some changes in the dependent variable (Robbins, 1998). 3. 4. 1. 1 Perceived Usefulness Perceived usefulness of the individuals was measured on six items using 5-point scale ranging from “strongly disagree” (1) to “strongly agree” (7). Items were derived from Park & Chen (2007). Example of question is “Using the smartphone would enable me to accomplish tasks more quickly”. 3. . 1. 2 Perceived Ease of Use Six items using 5-point scale was used to measure perceived ease of use of the individuals ranging from “strongly disagree” (1) to “strongly agree” (7). Items were derived from Park & Chen (2007). Example of question is “I would find it easy to get the smartphone to do what I want it to do”. 3. 4. 1. 3 Compatibility This measure was derived from Park & Chen (2007) and a total of 3 items was measure using 5-point scale ranging from “strongly disagree” (1) to “strongly agree” (7). Example of question is “Using the smartphone will be compatible with all aspects of my studies”. 3. 4. 1. Observability Observability of the individuals was measured on six items using 5-point scale ranging from “strongly disagree” (1) to “strongly agree” (7). Items were derived from Park & Chen (2007). Example of question is “It is easy for me to observe others using the smartphone in my university”. 3. 4. 1. 5 Trial ability This measure was derived from Park & Chen (2007) and a total of four items was measure using 5-point scale ranging from “strongly disagree” (1) to “strongly agree” (7). Example of question is “Before deciding on whether or not to adopt the smartphone, I would need to use it on a trial basis”. . 4. 1. 6 Self-Efficacy Self-efficacy of the individuals was measured on ten items using 5-point scale ranging from “strongly disagree” (1) to “strongly agree” (7). Items were derived from Park & Chen (2007). Example of question is “I could complete a task using the smartphone if I had seen someone else using it before trying it myself”. 3. 4. 2 Dependent Variable Dependent variables are variable that is measured, predicted, or monitored and are expected to be affected by the manipulation of the independent variable. The dependent variable for this study is the intention to use smartphones. 3. 4. . 1 Intention to Use Smartphones Intention to use smartphones was measured by items adopted and validate by Park & Chen (2007). It has a total of four items measuring the intention of users to use smartphones. Example of item is “Assuming I have the smartphone, I intend to use it”. 3. 4. 3 Moderating Variable Moderating variable is a second independent variable, believed to have a significant contributory or contingent effect on the originally stated IV-DV relationship. The moderating variable for this study is attitudes towards using smartphones. 3. 4. 3. 1 Attitudes towards Using Smartphones

Four items using 5-point scale was used to measure perceived ease of use of the individuals ranging from “strongly disagree” (1) to “strongly agree” (7). Items were derived from Park & Chen (2007). Example of question is “Using the smartphone is would be a pleasant experience”. 3. 5 Questionnaire Design One hundred and twenty five respondents from Universiti Sains Malaysia voluntarily responded and completed the questionnaire. The questionnaire has 10 sections with 55 questions to measure the relationship of those factors and the intention to use smartphones as well as some demography questions.

Table 3. 1 depicts that all instruments used in this study had a corresponding Cronbach alpha ;. 693 Table 3. 1 Questionnaire Source and Validity |Variable |Construct |Items |Cronbach | Author | |Independent |Perceived Usefulness |6 |;. 779 |Park & Chen (2007) | | |Self-Efficacy |10 |;. 85 |Park & Chen (2007) | | |Perceived Ease of Use |6 |;. 764 |Park & Chen (2007) | | |Trialability |4 |;. 748 |Park & Chen (2007) | | |Observability |2 |;. 693 |Park & Chen (2007) | | |Compatibility |3 |;. 99 |Park & Chen (2007) | |Dependent |Intention to Use Smartphones |4 |;. 765 |Park & Chen (2007) | |Moderating |Attitude towards Using Smartphones |4 |;. 795 |Park & Chen (2007) | 3. 6 Data Collection Technique Data for this study was collected through structured questionnaires. The questionnaires were distributed to students in USM, Penang. 3. 7 Statistical Data Analysis The data gathered through questionnaire was subsequently coded and analyzed sing the computerized SPSS (Statistical Software Package for Social Science) software version 16. They were summarized using appropriate descriptive and inferential statistics. 3. 7. 1 Goodness and Correctness of Data Entry Establishing the goodness of data lends credibility to all subsequent analyses and findings (Sekaran, 2003). The main objective is to provide an introductory idea of how good the scales were by checking the central tendency and distribution of the responses. In order to prevent data entry error, data will be checked by running descriptive statistics for minimum, maximum, and count.

The mean, range, standard deviation and variance in the data will give a good idea of how the respondents have reacted to items in the questionnaire (Sekaran, 2003). Nevertheless, the missing value does not exhibit whether the data had been entered correctly. This is due to the large amount of variables that need to be keyed in. 3. 7. 2 Factor Analysis The principle concern of factor analysis is the resolution of a set of variables linearly in terms of (usually) a small number of factors. This resolution can be accomplished by the analysis of the correlation among the variables.

A satisfactory will yield factors which concern essential information if the original set of variables (Harry H. Harman, 1976). When a researcher has a set of variables and suspects that these variables are interrelated in a complex fashion, then factor analysis can be used to untangle the linear relationships into their separate patterns (Zikmund, 2003). 3. 7. 3 Validity and Reliability Validity becomes an issue whenever we ask: How can we access a concept that we have? Validity test is the degree to which the test actually measures what it claims to measure (Gregory, 1992).

Reliability test is the degree to which tests is free from error in measuring and therefore yield consistent results. It is th extent which respondent can provide almost similar answer to the same or approximately the same question the same way each time. Test validity is requisite to test reliability. If a test is not valid, then reliability is moot. Validity test plays an essential role in order to test the goodness of measurement. Validity ensures the ability of a scale to measure the intended concept (Sekaran 2003).

However, reliability also very important because reliability deals with the accuracy and precision of a measurement procedure which is the respondent can answer the same or approximately the same questions the same way each time. In short, reliability is the “consistency” or “repeatability” of measurement. In order to assure that the variables are measured correctly and make sure that the respondent was understood the lucidness, wordings, interpretation and appropriateness of the questions, the content validity of the questionnaire was established through literature review.

Cronbach’s coefficient alpha is the commonly used measure for internal consistency reliability. Cronbach’s alpha assesses the reliability of a rating summarizing a group of test or survey answers which measure some underlying factor. Cronbach’s alpha value that larger than . 70 or . 80 regard as the benchmark for acceptable reliability values (Nunnally and Bernstein, 1994). 3. 7. 4 Descriptive Analysis The analysis aims to provide an overview of the respondents and an insight into their behavioural patterns. Descriptive analysis was not used to analyze gender, race, education and income level.

For this data, the frequencies and percentage was used for computation. 3. 7. 5Regression Analysis Regression analysis is used as a statistical tool for the investigation of relationships between variables (Norman R. Draper, Harry Smith, 1998). Multiple regressions are a statistical technique that allows us to predict someone’s score on one variable on the basic of their scores on several other variables. Below are the assumptions of regression analysis. a. Normality assumption Regression assumes that variables have normal distribution. It used to determine whether a random variable is normally distributed.

If the histogram appears to at least resemble a bell shape curve, it was assumed that the normality requirement has been met. A bell shape curve will have almost zero mean and value of one for standard deviation. b. Linearity assumption Standard multiple regression can only accurately estimate the relationship between dependant and independent variables if the relationship are linear in nature. Linearity illustrates a relationship between variables that can be described by a straight line passing through the data cloud. c. Homoscedasticity assumption

Homoscedasciticity means that the variance of errors is the same across all level of the IV. When the variance of errors differs at different values of the IV, heteroscedasticity is indicated. This assumption means that the variance around the regression line is the same for all values of the predictor variable. d. Independence of Error Term Independence of Error Term means the predicted value is independent of other predicted values. Durbin-Watson statistics was used to validate the independence of error term assumption. Value of Durbin-Watson should fall between 1. 50 and 2. 0, which implies no auto-correlation problem. e. Multicollinearity Multicollinearity is the condition when two or more of the independent variables are highly correlated which will result in an overestimation of the standard deviation of the regression coefficients as an indicator of the relative importance of independent variable. Tolerance above 0. 1, Variance Inflation Factor (VIF) value below 10 and condition index below 30 signifies no major multicollinearity problem. f. Outliers In statistics, an outlier is an observation that is numerically distant from the rest of the data.

Case wise diagnostics was run to identify any outlier in the sample. Any cases that fell above the standard deviation value of 2. 50 would be dropped. CHAPTER 4 ANALYSIS AND RESULT 4. 1 Introduction This chapter represents the result of the study from the statistical analysis conducted on the collected data and hypotheses testing. In the first part of this chapter the presentation would be on the characteristics of respondent profiles. The goodness of measured is determined by analyzing frequency analysis, descriptive analysis and reliability analysis on the measurement.

The final part of this chapter would be focused on hypotheses testing, correlation testing and linear regressions. 4. 2 Samples and Profiles 4. 2. 1 Frequency Analysis Table 4. 2. 1: Personal Profile of Respondents |Demographics |Frequency |Percentage | |Gender | | | | Male |43 |34. | | Female |82 |65. 6 | | Missing |0 |0 | |Ethnicity | | | | Malay |46 |36. 8 | | Chinese |65 |52. | | Indian |5 |4. 0 | | Others |9 |7. 2 | | Missing |0 |0 | |Nationality | | | | Malaysian |86 |68. | | Others |39 |31. 2 | | Missing |0 |0 | |Year | | | | First Year |31 |24. 8 | | Second Year |66 |52. | | Third Year |21 |16. 8 | | Fourth Year and Above |7 |5. 6 | | Missing |0 |0 | |Program | | | | Bachelor’s degree (undergraduate) |123 |98. | | Masters |2 |1. 6 | | Missing |0 |0 | |Status | | | | Part Time |17 |13. 6 | | Full Time |108 |86. | | Missing |0 |0 | |Faculty | | | | Management |95 |76. 0 | | Computer |6 |4. 8 | | Technology |4 |3. | | HBP |11 |8. 8 | | Communication |3 |2. 4 | | Chemistry |2 |1. 6 | | Humanities |1 |0. 8 | | Missing |3 |2. | |Live | | | | In Campus |100 |80. 0 | | Outside Campus |25 |20 | | Missing |0 |0 | A total of 125 responses were obtained from 125 questionnaires.

According to table 4. 2. 1, the respondents comprised 43 males (34. 4%) and 82 females (65. 6%). 46 (36. 8%) of the 125 respondents were Malay, 5(4. 0%) Indian, 65 (52. 0%) Chinese and other races comprised of 9 (7. 2%). 86 (68. 8%) of the respondents were Malaysians whereas 39 (31. 2%) of them are from other countries. Among the respondents, 31 (24. 8%) of them were First Year students, 66 (52. 8%) of them were Second Year students, 21 (16. 8%) of them were Third Year students and 7 (5. 6%) of them were students form Fourth Year and Above. Besides that, 123 (98. %) of the respondents were undergraduate whereas 2 (1. 6%) of them were master students. 17 (13. 6%) of the respondents were part time students whereas 108 (86. 4%) of them were full time students. In addition, 95 (76. 0%) of the respondents were students from School of Management, 6 (4. 8%) of them were students from School of Computer,4 (3. 2%) of them were from School of Technology, 11 (8. 8%) of them were from School of HBP, 3 (2. 4%) of them were students were students from School of Communication, 2 (1. 6%) of them were students from School of Chemistry, 1 (0. %) of them were students from School of Humanities and 2 (2. 4%) of the data were missing. 100 (80%) of respondents were live in campus whereas 25 (20%) of them were live at outside campus. Table 4. 2. 1. a Internet Experience of Respondents |Demographics |Frequency |Percentage | |Access | | | | Yes |117 |93. | | No |8 |6. 4 | | Missing |0 |0 | | | | | |Where | | | | Home |83 |66. | | Place of employment |13 |10. 4 | | School/ academic institution |21 |16. 8 | | Cybercafe |3 |2. 4 | | Others |5 |4. | | Missing |0 |0 | |Browser | | | | Internet Explorer |40 |32. 0 | | Mozilla Firefox |30 |24. | | Others |32 |25. 6 | | More than one browser |23 |18. 4 | | Missing |0 |0 | |Time | | | | Almost never |2 |1. | | From 0. 5 hours to 1 hour |5 |4. 0 | | 1-2 hours |17 |13. 6 | | 2-3 hours |31 |24. 8 | | More than 3 hours |70 |56. | | Missing |0 |0 | |Often | | | | Less than once a month |1 |0. 8 | | Once a month |1 |0. 8 | | A few times a week |13 |10. | | About once a day |30 |24. 0 | | Several times a day |80 |64. 0 | | Missing |0 |0 | According to table 4. 2. 1. a, 117 (93. 6%) of the respondents have internet access at home while 8 (6. 4%) of them do not have internet access at home. Other than that, 83 (66. %) of the respondents were primarily access internet from home, 13 (10. 4%) of them were primarily access internet from place of employment, 21 (16. 8%) of them were primarily access internet from school or academic institution, 3 (2. 4%) of them were primarily access internet from cybercafe and 5 (4%) of them were primarily access internet from other places. Internet Explorer was the most popular web browser used by respondents which recorded 40 (32%) of respondents following by 32(25. 6%) of them were using others web browser, and 30 (24%) of them were using Mozilla Firefox. 23 (18. %) of the respondents were using more than one browser. On an average day, 70 (56%) of the respondents were spend more than 3 hours on the internet, 31 (24. 8%) of them were spent 2-3 hours on the internet, 17 (13. 6%) of them were spent 1-2 hours on the internet, 5 (4/0%) of them were spent from 0. 5 hours to 1 hour on the internet and only 2 (1. 6%) of them almost never spending their time on the internet. On average, 80 (64%) of the respondents were using internet for several times a day, 30 (24%) of them were using internet for about once a day, 13 (10. 4%) of them were using internet for a few times a week, 1 (0. %) of them was using internet for once a month and another 1 (0. 8%) of them was using internet for less than once a month. 4. 3 Descriptive Analysis The summary of the descriptive statistic of the variables is given in table below. Table 4. 3. 1 Overall Descriptive Statistics of the Study Variables |Variables |Mean |Standard Deviation | |Perceived Usefulness | 3. 4707 |0. 56403 | |Self-Efficacy |3. 216 |0. 44948 | |Perceived Ease of Use |3. 6587 |0. 51145 | |Trialability |3. 5720 |0. 66510 | |Observability |3. 6280 |

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