INTENTION TO USE OF SMART PHONE IN BANGKOK EXTENDED UTAUT MODEL BY PERCEIVED VALUE

Size: px

Start display at page:

Download "INTENTION TO USE OF SMART PHONE IN BANGKOK EXTENDED UTAUT MODEL BY PERCEIVED VALUE"

Stephany Atkins
6 years ago
Views:

1 INTENTION TO USE OF SMART PHONE IN BANGKOK EXTENDED UTAUT MODEL BY PERCEIVED VALUE Krittipat Pitchayadejanant 1 * ABSTRACT Smart phones, iphone and Black Berry, are very popular among new generation people in Thailand. The advantages in these technologies are joining with social network such as Facebook, Twitter and etc. There are many useful applications that most users enjoy like games, camera which can be downloaded easily. Moreover, it saves money for spending calling cost when they use the free application to call each other even thought they are in different country. The features of these two smart phones are trendy and fashionable. Also, the network to connect between smart phone and social network sites, supports these technologies. This study is used to interview and respondents questionnaire. The study also investigates the significant of Perceived Value and study the different between people who use iphone and Blackberry. Therefore, the group of respondents is classified into two groups; iphone users and Blackberry users. The technique to be used to analyze the result is Structural Equation Model (SEM). Keyword: Smart Phone, UTAUT model, Perceived Value, Technology Acceptance INTRODUCTION Due to the present lifestyle of mobile usage is emphasizing on Mobility ability that effect to the growth of high-end market especially in Asia so that Research Company concludes that Asia has the highest growth rate of mobile used. Smartphone is a new technology that satisfies more need of customers, Thai has tendency higher rate of smart phone used especially the second mobile of customers. From the past, more than fifty percent of number of mobile used is smart phone and tendency change to use a new higher feather smart phone like market world tendency. Growth in demand for advanced mobile devices boasting powerful processors, abundant memory, larger screens and open operating systems has outpaced the rest of the mobile phone market for several years. The below table shows the global smart phone vendor market share. The total market share (million dollars), has increased from year 2008 and 2009 continuously. 1 * Krittipat Pitchayadejanant, Department of Business Data Analysis, Faculty of Science and Technology, Assumption University krittipat@scitech.au.edu 160

2 Table 1: Smartphone sale volume report of Cellular News website Global Smartphone Vendor market share (million dollar) 4 th Quarter 2008 Year th Quarter 2009 Year 2009 Nokia RIM (BB) Apple (iphone) Others Total Source: According to a study by ComScore, in 2010, over 45.5 million people in the United States owned Smartphone and it is the fastest growing segment of the mobile phone market, which comprised 234 million subscribers in the United States. The illustrate reasons of increasing in number of smart phone used: it can be used similarly to a small computer because users can search information, play games and create note/organize and etc. Moreover, they can browse internet, online website and chat which are easier than ordinal mobile phone. Smart Phone characteristics A Smartphone is a mobile phone that offers more advanced computing ability and connectivity than a basic 'feature phone'. While some feature phones are able to run simple applications based on generic platforms such as Java, ME or BREW, a Smartphone allows the user to install and run much more advanced applications based on a specific platform. Smart phones run complete operating system software providing a platform for application developers. Smart Phone in Thailand Nattawat Woranopakul said A highly competitive market and low smart phone prices has resulted to grow at almost 50% this year to value 12.5 billion baht by the average smart phone price is expected to drop to 12,500 baht this year, down from 15,000 last year and 20,000 in 2008 reported by Bangkok Post on 7 April The top five selling smart phone brands in Thailand are: Nokia, BlackBerry, Samsung, iphone and HTC. Smart Phone is new technology in Thailand and high competitive in the market. Then, the objective of this research is studying the acceptance of Smart Phone in Thailand based on UTAUT model. 161

3 LITERATURE REVIEW According to Unified Theory of Acceptance and Use of Technology (UTAUT) model (Venkatesh V. et.al. 2003) has been discussed in various field in this decade such as Educational Technology (Thanaporn, 2003), E-government Service (Suha A., Anne M., 2008), Internet Banking Adoption (Kholoud Al-Qeisi, 2003), 3G Mobile Communication (Yu-Lung Wu & Yu-Hui Tao & Pei-Chi Yang, 2006) and etc. The UTAUT model (Venkatesh V. et.al. 2003) has been integrated by Theory of Reasoned Action (TRA); Theory of Planned Behavior (TPB); Technology Acceptance Model (TAM); Combined TAM and TPB (C-TAM-TPB); Innovation Diffusion Theory (IDT); Model of PC Utilization (MPCU); Motivation Model (MM) and Social Cognitive Theory (SCT). This model is developed and validated in North America and has not been revalidated in other cultures (Said S., 2006). The UTAUT model consists of four determinants of user acceptance and technology usage; Performance Expectancy; Effort Expectancy; Social Influence; and Facilitating Conditions. Figure 1: Unified Theory of Acceptance and Use of Technology model (UTAUT) Source: V. Venkatesh et. al Performance Expectancy Performance Expectancy is defined as the degree to which an individual believes that using the system will help him or her to attain gains in job performance (Venkatesh et al., 2003). Performance Expectancy is combined with five root constructs: Perceived Usefulness (Davis 1989; Davis et al. 1989); Extrinsic Motivation (Davis et al. 1992); Job- Fit (Thompson et al. 1991); Relative Advantage (Moore and Benbasat, 1991); and Outcome Expectations (Compeau and Higgins 1995b; Compeau et al. 1999). 162

4 The smart phone users expect the performance of smart phone to attain the job and increase the job performance. Smart phone has been developed continuously to fulfill the requirements of users to improve their job performance. For illustrate, there are many applications; Outlook, MS Word, MS Excel etc., for users to contact their customers in order to their business running. Thus, performance expectancy impacts the behavior intention to use smart phone. Effort Expectancy Effort Expectancy is defined as the degree of ease associated with the use of the system (Venkatesh et al., 2003). This determinant has been combined with three root constructs: Perceived Ease of Use (Davis 1989; Davis et al. 1989); Complexity (Thompson et al. 1991); and Ease of Use (Moore and Benbasat, 1991). Effort Expectancy, perceived ease of use, is another interesting determinant to describe the behavior of smart phone users. NAR s Center for Realtor reported Ease of Use (26.2%) is top three of respondents who were motivated to choose the current smart phone (2009). According to research mentioned above, smart phone producers spend vast amount to develop their products for ease of use. Thus, perceived ease of use in smart phone effects making decision of users. Social Influence Social Influence is defined as the degree to which an individual perceives that important others believe he or she should use the new system (Venkatesh et al., 2003). Three constructs has been combined to be endogenous variables: Subjective Norms (Ajzen 1991; Davis et al. 1989; Fishbein and Ajzen 1975; Mathieson 1991; Taylor and Todd 1995a, 1995b); Social Factors (Thompson et al. 1991); and Image (Moore and Benbasat 1991). In recent research from NAR s Center for Realtor (2009), reported that the most smart phone application category they used is Social Media application e.g. Twitter, Facebook and etc. Consequently, smart phone users decide to use smart phone to keep contacting with their friends, colleagues or others in social network sites. Facilitating Conditions Facilitating Conditions are defined as the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system. Three constructs embody this determinant: Perceived Behavioral Control (Ajzen 1991; Taylor and Tood 1995a, 1995b); Facilitating Conditions (Thompson et al. 1991); and Compatibility (Moore and Benbasat 1991). In Thailand, the infrastructure exists to support the use of smart phone such as WiFi and 3G. Therefore, many companies support the employee to use smart phone to keep contact while they are asking for help which is more comfortable and convenience. 163

5 Their employees can search information or receive pushed mail from their head quarter rapidly. Behavior Intention Behavior Intention is a measure of strength of one s intention to perform a specified behavior (Fishbein and Ajzen, 1975). It is a predictor for usage (Szajna, 1996). The measurement of behavioral intention included the intention, prediction and planned use of technology (Suha A and Anne M, 2008). The Behavioral Intention can be use to describe the Actual Use because there was empirical study and had significantly correlated with Actual Use (Davis, 1989). Thus, this research paper intends to use Behavioral Intention as dependent variable instead of Actual Use. In this study, the UTAUT model is used as the baseline model to verify the relationship towards behavioral intention in the context of smart phone. There is another determinant presented into this study is Perceived Value. Perceived Value Value can be a good predictor of behavioral intentions (Andreas and Wolfgang, 2002). Two main consequences of Value reported; satisfaction and intention (Ledden L. et al., 2007). Ledden L. et al. studied the impact of Value on Satisfaction. Thus, this research article studies the impact of Perceived Value on Behavioral Intention. Value is a subjective construct which mean the different person will perceive value differently even the same product. For illustrate, the smart phone has value for some users but it might not be valuable for some users. However, the level of value perception is different in different users. Perceived value is a trade-off between benefits and sacrifices (Zeithaml, 1988, p.14; Monroe, 1990, p.46). Perceived benefits are the combination of physical attributes, service attributes and technical support in relation to a particular use (Monroe, 1990). Perceived sacrifices are more broad not only monetary terms. The dimensions of value for nonmonetary terms are classified into five categories as follows: Functional Value (Stafford, 1994; LeBlanc and Nguyen, 1999), Social Value (LeBlanc and Nguyen, 1999), Epistemic Value (Stafford, 1994), Emotional Value (LeBlanc and Nguyen, 1999) and Conditional Value (Unni, 2005). Perceived Value in this paper is combining the monetary value and non monetary value. Monetary value concerned on value of money they spend for purchasing smart phone while non monetary value concerned on emotional value such as fun, happiness to use smart phone. 164

6 This study wants to focus on studying impact of Performance Expectancy, Effort Expectancy and Social Influence impact on Perceived Value. In usual, people intend to purchase the product when they found that the product is useful, completes their needs and feeling in social community. Then, they impact to value that they perceived value from money spend and fun. Study Device There are many types and brands of Smart Phone in Thailand. Then this study focuses only iphone and Blackberry which are most popular in present. Based on the benefit of general smart phone, iphone and Blackberry can attract the users in design and outstanding functions. There are many applications provided from service provider to download. Then, the competitions among dealers to offer the price and service of these two smart phones in order to gain market share are high and interesting to study. Participant in this Study In this research, we use the non-probability sampling technique to generate our target sample because the researcher cannot acquire the list of all people, called as sampling frame, who have iphone and Blackberry in Bangkok area. Thus the probability of respondent chosen is unknown; the non-probability sampling is more suitable than probability sampling. Consequently, Quota sampling is employed by using 204 respondents for each kind of technologies, iphone and Blackberry, intended to study. Then, the target population of this research is all people who have used smart phone in Bangkok. The questionnaire has been distributed in 4 major areas in Bangkok. RESEARCH MODEL AND HYPOTHESES According to Literature Review, research model in this study can be designed 8 hypotheses. Hypothesis 1 - the consumers of smart phone think that Performance Expectancy has significantly positive relationship with the Behavior Intention to use smart phone. Hypothesis 2 - the consumers of smart phone think that Effort Expectancy has significantly positive relationship with the Behavior Intention to use smart phone. Hypothesis 3 - the consumers of smart phone think that Social Influence has significantly positive relationship with the Behavior Intention to use smart phone. Hypothesis 4 - the consumers of smart phone think that Facilitating Condition has significantly positive relationship with the Behavior Intention to use smart phone. 165

7 Hypothesis 5 - the consumers of smart phone think that Perceived Value has significantly positive relationship with the Behavior Intention to use smart phone. Hypothesis 6 - the consumers of smart phone think that Performance Expectancy has significantly positive relationship with the Perceived Value to use smart phone. Hypothesis 7 - the consumers of smart phone think that Effort Expectancy has significantly positive relationship with the Perceived Value to use smart phone. Hypothesis 8 - the consumers of smart phone think that Social Influence has significantly positive relationship with the Perceived Value to use smart phone. Figure 2: Model developed for this study DATA ANALYSIS AND RESULTS Four hundred and eight questionnaires have been distributed. Consequently, results of them have been analyzed as follow. According to Table 2, the demographic characteristics of all respondents are studied. The frequency between Male and Female who use Smartphone is not much different, 47.3% and 52.7%, respectively. Most of them are years old (35.0 %) and less than 18 years old (27.9%). The majority education level of those who response this questionnaire is Bachelor degree (55.1%). Mostly, the occupation in this study is Student (62.7%). 166

8 Analysis Validity and Reliability The reliability of questionnaire has been assessed in each composition: 6 items of Perceived Value (PV), 5 items of Facilitating Condition (FC), 4 items of Social Influence (SI), 5 items of Performance Expectancy (PE) and 4 items of Effort Expectancy (EE). Then, the reliability values (Cronbach s Alpha) are shown as 0.881, 0.905, 0.865, and respectively as shown in Table 3. As regard, all Cronbach s Alpha values are above 0.70 which implies all internal consistency of the survey instruments is acceptable and reliable (Nunnally and Bernstein, 1994). Table 2: Demographic Characteristics of Respondents Demographic Characteristics Frequency Percent Gender Male Female Total Age Less than 18 years old years old years old years old More than 45 years old Total Education Below Bachelor Degree Bachelor Degree Master Degree or above Total Occupation Student Government Officer Private company officer Self-employed Other Total Table 3: Results of Rotated Factor Loading and Cronbach s Alpha 167

9 Analysis of Structural Model The overall construct of this research model is tested and well fit because the result of each criterion meets the range of acceptance as recommended in below table. Table 4: Summary of Model Fit Model Fit Index Criteria Model 2 χ /df GFI AGFI NFI CFI RMR RMSEA The structural model in figure 3, shows the coefficients between construct of this research model. There are 5 causal paths are significant: PE -> PV (coef. = **, p = 0.005), EE -> PV (coef. = *** ), SI -> PV (coef. = *** ), FC -> BI (coef. = *** ) and PV -> BI (coef. = *** ) ( ** indicates p-value < 0.01, *** indicates p-value < 0.001). 168

10 The causal paths that are not significant consist of 3 constructs: PE -> BI, EE -> BI and SI -> BI. Figure 3: Graphical representation of Coefficient Value Hypothesis Testing Results The relationships of all construct are positively association between (1) Performance Expectancy and Perceived Value; (2) Effort Expectancy and Perceived Value; (3) Facilitating Conditions and Perceived Value; (4) Facilitating Conditions and Behavior Intention and (5) Perceived Value and Behavior Intention. The conclusion of hypotheses testing in this research has been depicted in Table 4. Table 4: Results of Hypothesis Testing Hypotheses Conclusion H1: Performance Expectancy has significantly positive relationship with the Behavior Intention Do not Supported H2: Effort Expectancy has significantly positive relationship with the Behavior Intention Do not Supported H3: Social Influence has significantly positive relationship with the Behavior Intention Do not Supported H4: Facilitating Condition has significantly positive relationship with the Behavior Intention Supported H5: Perceived Value has significantly positive relationship with the Behavior Intention Supported H6: Performance Expectancy has significantly Supported 169

11 positive relationship with the Perceived Value H7: Effort Expectancy has significantly positive relationship with the Perceived Value H8: Social Influence has significantly positive relationship with the Perceived Value Supported Supported CONCLUSION The Behavior Intention to use smart phone depends on Perceived Value and Facilitating Conditions. From the result, Performance Expectancy, Effort Expectancy and Social Influence do not directly impact to Behavior Intention. They are significant impact to Perceived Value. Consequently, Perceive Value is the mediating variable between Behavior Intention with Performance Expectancy, Effort Expectancy and Social Influence. This implies that smart phone users do intent to use their smart phone when they found its value, suitable with their money that they spend. Smart phone users do not focus only Performance Expectancy, Effort Expectancy and Social Influence individually but they integrate perception among these three components together to be Perceived Value of smart phone. Among these three components, the Effort Expectancy is the most significant impact Perceived Value. It implies the Smart Phone producers should realize on difficulty to use its functions. Otherwise, users do not purchase the Smart Phone. Moreover, Facilitating Conditions is also significant impact to Behavior Intention. Since Bangkok area, the infrastructure facilities, to support users use smart phone, are ready. Moreover, the organization or colleagues also use the smart phone together. Consequently, users are comfortable to use for completing their works and save time to dealing their work with colleagues. It implies that the users are ready to use smart phone when they found that the facilities to supporting usage has completed. REFERENCES 1. Ajzen, I. (1991) The Theory of Planned Behavior, Organizational Behavior and Human Decision Processes (50:2), pp AlAwadhi, S., Morris, A., (2008) The Use of the UTAUT Model in the Adoption of E-Government Services in Kuwait, Hawaii International Conference on System Sciences, Proceedings of the 41st Annual. 170

12 3. Compeau, D.R., and Higgins, C.A., (1995b) Computer Self-Efficacy: Development of a Measure and Intial Test, MIS Quarterly (19:2), pp Compeau, D.R., and Higgins, C.A., and Huff, S. (1999) Social Cognitive Theory and Individual Reactions to Computing Technology: A Longitudinal Study, MIS Quarterly (23:2), pp Davis, F.D. (1989) Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, MIS Quarterly (13:3), pp Davis, F.D., Bagozzi, R.P., and Warshaw, P.R. (1992) Extrinsic and Intrinsic Motivation to Use Computers in the Workplace, Journal of Applied Social Psychology (22:14), pp Davis, F.D., Bagozzi, R.P., and Warshaw, P.R. (1989) User Acceptance of Computer Technology: A Comparison of Two Theoretical Models, Management Science (35:8), pp Fishbein, M., and Ajzen, I. (1975) Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Addison-Wesley, Reading, MA. 9. Kholoud I.A., (2009) Analyzing the Use of UTAUT Model in Explaining an Online Behaviour: Internet Banking Adoption,. 10. LeBlanc, G. and Nguyen, N. (1999) Listening to the consumer s voice: examining perceived service value among business college students, International Journal of Educational Management, pp Ledden, L., Kalafatis, S.P. and Samouel, P. (2007) An empirical examination of the relationship between personal values and perceived value of education, Journal of Business Research, pp Mathieson, K. (1991) Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior, Information Systems Research (2:3), pp Monroe, K. (1990) Pricing: Making Profitable Decisions, McGraw-Hill, New York, NY. 14. Moore, G.C., and Benbasat, I. (1991) Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation, Information Systems Research (2:3), pp Nunnally, J. and I. Bernstein, (1997) Psychometric Theory. 3 rd Edition, McGraw- Hill, New York, ISBN-10: X. 171

13 16. Stafford, T.F. (1994) Consumption values and the choice of marketing electives: treating students like customers. Journal of Marketing Education, (16:2), pp Szajna, B. (1996) Empirical Evaluation of the Revised Technology Acceptance Model, Management Science (42:1), pp Taylor, S., and Todd, P.A. (1995a) Assessing IT Usage: The Role of Prior Experience, MIS Quarterly (19:2), pp Taylor, S., and Todd, P.A. (1995b) Understanding Information Technology Usage: A Test of Competing Models, Information Systems Research (6:4), pp Thompson, R.L., Higgins, C.A., and Howell, J.M. (1994) Influence of Experience on Personal Computer Utilization: Testing a Conceptual Model, Journal of Management Information Systems (11:1), pp Thompson, R.L., Higgins, C.A., and Howell, J.M. (1991) Personal Computing: Toward a Conceptual Model of Utilization, MIS Quarterly (15:1), pp Unni, R.M. (2005) Value perceptions and retention of textbooks among marketing and other business majors, Marketing Education Review, (15:2), pp V. Venkatesh et. al., (2003) User Acceptance of Information Technology: toward a unified view, MIS Quarterly, vol. 27, pp Wolfgang, U. and Andreas, E., Relationship Value in Business Markets: Development of a Measurement Scale, ISBM Report Zeithaml, V.A. (1988) Consumer perceptions of price, quality and value: a means-end model and synthesis of evidence, Journal of Marketing, vol. 52, pp

User Acceptance of E-Government Services

Association for Information Systems AIS Electronic Library (AISeL) PACIS 2007 Proceedings Pacific Asia Conference on Information Systems (PACIS) 2007 User Acceptance of E-Government Services Ying-Hsun