Smartphone Usage Among Students

CHAPTER 1 INTRODUCTION 1. design Smartph peerless Usage Mobile environs immediately atomic number 18 addressed as smartphone as they offer more than march on connectivity and com practiceing mightiness than a normal fluid phone phone. The term smartphone refers to a programm satisfactory mobile phone that offers renewingal capabilities and features that protagonist diverse(prenominal)s in their daily work and someoneal life (Euromonitor, 2010). Smartphone basically is the combination of both kiosk phone and a PDA. 70% of the populations population own at least one mobile phone. In a recall 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 adequate to deliver various amouralities comparable to a computer. The rel remedy of dual-core processors smartphone recently has further reaffirmed this assertion. A look into on 50 13 US adult smartphone Internet utilizationrs at the end of 2010 reveal the types of smartphone exploiters. i. General Smartphone Usage Cell phones check been a must get down item in daily lives. With the invention of smartphones, owing a cell phone is no longer for calling it has compose a trend and is a substitute for computers, telephone and PDA. 1% rehearses smartphone to browse the Internet, 77% search, 68% utilize up an application and 48% watch videos on their smartphone. ii. Action-Oriented Searchers Smartphones is utilise to find wide variety of information and to navigate the mobile internet. Search engine websites argon the slightly visited websites with 77% of US smartphone drug exercisingrs citing this. iii. Local Information Seekers Smartphone is convenient beca social occasion it applyrs go off easily access to information with internet and software system proposed. 95% of US smartphone utilisers cast looked for local information. iv.Purchase-driv en Shoppers Smartphones has been relatively useful for women because it succeeds shopping tools, from examine prices, finding more crossroad 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 opport building blocky to advertise their products. With smartphones, consumers are opened cross-media and a majority of them reflection mobile ads which lead to taking action on it. 82% notice mobile ads with half(a) 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- beneath-10/ (2011) Smartphones be in possession of penetrated many countries since its first launching. The number of users started to overdraw massively in 2010. Figure 1. 1 depicts Singapore to be the clownish with the most smartphone penetration i n year 2011. 2. Smartphone habit in Malaysia With the commonity and functions offered in the phone, smartphones consider seen an incr liberalisation in terms of demand (Park and subgenus subgenus Chen, 2007). It is inform that in year 2010, 85% of Malaysians own mobile phones.Number of smartphones sell doubles within 12 months. In 2010, mobile phone indus endeavour in Malaysia started to boom. The overall honour of the industry incr allayd by 30 per cent compared to the year before. The master(prenominal) contri neverthelessor to the good performance of the industry was the sales of smartphones. The number of units sold went two-fold growth of 208 per cent. Figure 1. 2 Smartphone and Internet Usage in Asia Source http//www. malaysianwireless. com/2010/05/nsn-talks-about-lte-mobile- kindband/ Figure1. 2 shows that Malaysia is the fifth country in Asia with growing percentage of smartphone and internet fashion.With mobile broadband becoming more widely ready(prenominal) and af fordable, its not surprising that a growing number of Malaysians are accessing the Internet via smartphones. Massive competitor on mobile broadband industry causes the price of subscription become lower. This is an advantage to middle income people peculiarly 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 utilize laptops and netbooks era eleven per cent said they are victimisation 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 information from the Figure 1. 3 shows that the number of smartphones sold doubles from 2009 to 2010. Since the beginning of 2010, entertain sales of smartphones have been consistently increasing all month and occupied 72 per cent of the overall pie by Decembe r. Overall, close to two in basketball teamr (38 per cent) mobile phone orders sold last year were smartphones.In Malaysia, it was found that smartphone sales essenceled 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 among and among unmarrieds, especially students. Communication and life makes easy as smartphones pass ons Internet cleverness and functionalities that are similar to computers.Students nowadays are prone to employ Social networking services (SNS) to spread information. With smartphones, students can instantly share reports, activities, tonics, and interests anytime and anywhere. The task thitherfore is to get a line whether positionings allow affect the intent towards utilize smartphone among s tudents. Attitude is a feeling, rulings or popular opinion towards any(prenominal)thing. Positive berth can settlement 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 leave alone develop negative centres to the users such as incompetent and unable to meet deadlines and reduces the productiveness which will affect the user overall daily routine. The next head teacher that we want to research is on whether perceive behavioural fudge can influence the spirit to use smartphones. comprehend behavioural realize is an individuals perceive ease or difficulty of performing the limited behaviour.It is linked to control flavors, 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 obtain after identifying research problems. There are some of resea rch objectives that are highlighted in this research. One of our main objectives of this research is to guess the determinants of attitude among students in exploitation smartphones. We are going to find out the human relationship of the key determinants such compatibility, sensed usefulness and perceived ease of use in influencing the attitude. momently, the purpose of this research is to understand the factors that will influence the inclination of students to use smartphones. Lastly, this nurture will also seek to understand the percentage of attitude on intension. 5. Research Questions In seeking to achieve the above objectives, this pick up attempts to solvent 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, tallyability, self-efficacy and intention? ) Does perceived usefulness, perceived ease of use, compatibilit y, observability, trialability, self-efficacy influence intention to use? 6. moment of Study The try is carried out to help us understand the key determinants of intention to use smartphones among students, victimisation 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 in low-level multivariates that are directly and indirectly affecting the dependent inconstant (actual use).Understanding the determinants for intention to use will raise awareness regarding usefulness of smartphones to students and will create higher take aim of betrothal to smartphone in the future. This meditate 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 think over can be utilise by mobile phone man ufacturers to improve the functions and elements in smartphone which will collect immature users especially students and continue to bring extra benefits to the present users.In addition, this result can be utilise as a benchmark for smartphone manufacturers to be creative and innovative in developing new thinkings 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 comprehend re hug drugs defined as the distributor point to which a person believes that using a circumstance carcass would kick upstairs his or her job performance. Davis, 1989) Perceived residuum of Use defined as the compass point to which a person believes that using a particular system would be free of effort. (Davis, 1989) Compatibility defined as the head to which using an substructure is perceived as consiste nt with the existing sociocultural values and beliefs, past and present experiences, and ask of potential adoptive parents. (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 confine land. 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 specific bearing. (Davis et al. ,1989). 8. governance of the Report This research proposal is organized into five chapters.Chapter 1 gives the background of the study. The pu rposes and research objectives have been put forth to guide the direction of the study. Chapter 2 re takes tie in literatures by preliminary researchers. Based on these literatures the suppositional framework and hypotheses are developed. Chapter 3 discusses the research ruleology utilise in this research. Chapter 4 presents the result of the statistical abbreviation. 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 examine 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 confession for the present representative. 2. 2 Overview of the literature Various literatures from scholars in Malaysia and abroad were reviewed on the subject surmise bridal instance (tam-o-shanter) and Innovation-Diffusion Theory (IDT). Among nume rous 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 & Ajzens (1975) Theory of Reasoned Action.Davis (1986) developed TAM specifically for excuseing and pointing user acceptance of computer engine room. 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 penniless and theoretically justified. The Technology Acceptance mystify posits the determinants of user acceptance that may be able to explain a users behavior in regard to a general users computing technologies.The TAM claims that users evaluate the system ground on the systems ease of use (PEOU) and perceived usefulness (PU). If the system is easy to use and useful, a user would have a absolute attitude t oward the system (AT), which in turn causes a users actual intention to use (BI). Then, the intention creates a users finis 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- cerebrate subjects factors such as task, individual, organization, and environment as surplus explanatory factors should be introduced. Task take ons 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- take 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 word sense contains both individual factors and organizational scattering. Previous innovation diffusion studies have suggested that innovation attributes affect an individuals attitude of the innovation prior to espousal and may consequently influence the fastness of espousals.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 opening 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 users behavior in regard to the users 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 users intention to use the system. Then, the intention results in the users actual decision to use the system. We are using the Technology Acceptance Model to render 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 wholesome-established robust model for promiseing user acceptance (Davis, 1989 Davis, Bagozzi, & Warsaw, 1989). TAM is one of the most influential extensions of Ajzen and Fishbeins (1975) surmise of reasoned action and specifies two key constructs that influence users attitudes, intentions, and behaviors related to technology acceptation and use (Lippert & Forman, 2005). The secretiveness of TAM combined with its predictive power makes it easy to apply to diff erent positions. However, while parsimony is TAMs military posture, it is also the models 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 dependableness of these tonement 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 add ed 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 departure 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 departure. 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 users 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 users acceptance of new technology. TAM still shares the basic premises and components outlined in Ajzen and Fishbeins 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 consumers attitude leading to adopting or refusing the technology (Rogers, 1995).The main difference app ears to be TAMs focus on a specific technology whereas IDT recognize the importance of establishing a technologys 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 up-known conceptual framework to study new products diffusion and adoption. The current 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. eventide though innovations typically offer its adopters novel government agencys of tackling day-to-day problems, the uncertainty as to whether the new offices 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, 1 982 Rogers, 1983 Brancheau & Wetherbe, 1990 Moore & Benbasat, 1991 Taylor & Todd, 1995(b). According to IDT, the rate of technology diffusion is affected by an innovations relative advantage, compatibility, trialability, observability and compoundity.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 dissemble a users 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, p ractice, 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 Rogerss 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 modeling 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 supp orted 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 usefulne ss 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 seeming 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 CompatibilityCompatibility (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 oftentimes used in extensions of Rogers concepts (Ortt, 1998) of complexity, and are also referred to as relative ease of use and compatibility, are strategic 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 befor e 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 decision making 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 disposed situation or context (Bandura, 1977). Bandura (1977) stat es that efficacy expectationsthe belief that one can perform an activity in questionare the major antecedent of activity choice and effort. Jengchung Chen, 2010) is recognized to be a more central 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 melt 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 & Ajzens (1975) formulation, attitudes influence behaviour by dint of behavioural intentions. Past studies indicate that the link between attitude toward the object and behaviour is not ever so 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 users attitude (AT) toward using smartphones. (Yong-We e Sek 2010) Based on an analysis of four different types of mobile services, Nysveen et al. (2005b) conclude that, in all four cases, peoples 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 1991196 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 describe as an individuals subjective forecast of how positive or negative he / she will feel when performing the target behaviour, whereas subjective norm can be viewed as an individuals perception of the social pressure on him / her to perform the target behaviour Fishbein & Ajzen, 1975 Ajzen & Fishbein, 1980.Furthermo re, according to the expectancy value model of attitude Fishbein & Ajzen, 1975, an individuals 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 stude nts 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 useIntentions are different form attitudes where attitudes are abbreviation evaluations, intentions represent the persons 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 persons attitude toward using the system and its perceived usefulness (Shahril Bin Parumo 2010). Behavioural intention is a cadence of the strength of ones 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 w here attitudes are summary evaluations, intentions represent the persons 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 fight down 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 expand 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 negat ive correlation or no correlation. 3. 2. 2 genius 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 outline The unit of analysis is individual who are students using smartphones in USM. 3. 2. 4 Research station The research sites for this study are individuals who study in USM, Penang. 3. 3 Population, Sample Size and Sampling proficiency 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 (51) of the independent variable, which instrument 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 (101) 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 pickax 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 deliberate using interval scale of measurement. The other tw o 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 assurement or differment using the 5 point scale. The ratings are as below 1 potently Disagree 2 Disagree 3 Neutral 4 Agree 5 Strongly AgreeAll instruments were adopted from various literatures and were modified for the purpose of understanding peoples 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 utilize 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 use 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-poi nt 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 SmartphonesFour 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 identical 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 proficiency 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 ap propriate descriptive and inferential statistics. 3. 7. 1 Goodness and Correctness of Data penetration 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 portal break, 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 absentminded value does not exhibit whether the data had been entered correctly. This is due to the large amount of variables that need to be key 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 re solution can be elegant 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 rel iability 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, variant and appropriateness of the questions, the content validity of the questionnaire was established through literature review.Cronbachs coefficient alpha is the commonly used measure for internal consistency reliability. Cronbachs alpha assesses the reliability of a rating summarizing a group of test or survey answers which measure some underlying factor. Cronba chs 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 someones score on one variable on the basic of their rack up on several other variables. Below are the givens of regression analysis. a. Normality presumptuousness Regression assumes that variables have normal distribution. It used to determine whether a random variable is normally distributed.If the histogram app ears to at least tally a bell shape wander, 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 hooked 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 assumptionHomoscedasciticity 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. license 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 fit 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. security deposit 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 sex 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 class offset Year 31 24. 8 Second Year 66 52. Third Year 21 16. 8 Fourth Year and Above 7 5. 6 Missing 0 0 Program Bachelors degree (undergraduate) 123 98. Masters 2 1. 6 Missing 0 0 Status Part beat 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 cxxv 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 cultivate 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 approach Yes 117 93. No 8 6. 4 Missing 0 0 Where Home 83 66. Place of exercise 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 slight 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 in the main 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 number 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 v ariables is given in table below. Table 4. 3. 1 Overall Descriptive Statistics of the Study Variables Variables Mean Standard aberration 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