EXPLORING TECHNOLOGY ADOPTION FACTORS OF WEB SEARCH ENGINES INFLUENCING TO USERS IN THAILAND Patthawadi Pawanprommaraj, Supaporn Kiattisin and Adisorn Leelasantitham Department of Technology of Information System Management Faculty of Engineering, Mahidol University, Thailand patthawadi.paw@student.mahidol.ac.th, Tel. +66 83049 5519 ABSTRACT The aim of this research is to present population web search engines in Thailand that is well known Google using the Unified Theory of Acceptance and Use of Technology (UTAUT). The UTAUT is to explore the technology adoption factors of web search engines influencing to user behavior in Thailand. The model comprises of four core variables and four modulating variables. Hence this theory is used as the model to find out by questionnaire investigation via the online document is called Google Docs in total based on scale 250 users. Significant moderating influences of Performance expectancy and effort expectancy are direct determinants of Behavioral Intention. Also the features on web search are influenced to use behavior as assumption. Keyword: Search Engine, UTAUT Model, and Google I. INTRODUCTION A. Background and Problem Statement Referring to internet usage statistics on September 2012 from Infographic: Thailand Digital Statistic by wilas.chamlertwat.in.th has shown the most popular of Search Engine in Thailand is Google, the frequency of daily usage approximately amount 19.2 million times. And the information from website: dated June 30, 2012 According to Internet World Stats (http://www.internetworldstats.com/stats3.html), Thailand is ranked 9th amongst countries in Asia, in terms of number of Internet users. ISS 462
Asia Top Internet Countries June 30, 2012 China 538 India Japan 101.2 137 Indonesia Korea, South Philippines Vietnam Pakistan Thailand Malaysia 55 40.3 33.6 31 29.1 20.1 17.7 Figure 1 Amount of Internet users in Asia estimated for June 30, 2012 In 2015, the important factor which affects the growth of technology industry is to be ready for ASEAN Economic Community (AEC). Across Asia, Thailand has one of the lowest internet penetration levels. This level of penetration coupled with the infrequent rate at which consumers are accessing the internet reinforces that digital media has significant growth opportunities in Thailand. B. Objectives The objectives of this study are as follow. 1. To explore the factors influencing to the technology adoption. 2. To study the external variables to direct determinants to behavior intention and use behavior to web search engines in Thailand ISS 463
C. Scopes of work 1. This research collected data from the internet users in Thailand via email invitation and social network is called Facebook. II. LITERATURE REVIEW A. To study the search engines A web search engine is a software system that is designed to search for information on the World Wide Web. The search results are generally presented in a line of results often referred to as search engine results pages (SERPs) [1]. In 2003, Hans is study the factor influencing the usage of website: the case of generic portal in The Netherlands. Using the technology acceptance model (TAM) to explain the individual acceptance and usage of websites. The data fully supported the model and all hypotheses can not reject. The paper has contributed to the original TAM by successfully introducing the concept of visual attractiveness [2]. In 2004, Daniel & Danny from Yahoo Inc. is studying on understanding user web search behavior focused on how users search and what they are searching for. They illustrate how this knowledge of user search goals will improve future web search engines [3]. In 2006, Eugene and Microsoft research team are study incorporating user behavior data can significantly to the top results in web search engine. Exploring the contributions of user feedback compared to other common web features with a large scale results over 3,000 queries and 12 million users. They had shown that implicit user feedback can further improve web search engines performance, when incorporated directly with popular content and link-based features [4]. ISS 464
B. The Unified Theory of Acceptance and Use of Technology (UTAUT) Figure 1 Unified Theory of Acceptance and Use of Technology (UTAUT) Source: Venkatesh, V., Morris, M.G., Davis, F.D., and Davis, G.B. User Acceptance of Information Technology: Toward a Unified View, MIS Quarterly, 27, 2003, 425-478. The study of UTAUT model is a behavioral model that aims to explore the behavior of human to technology acceptance in their use. This model contains four core variables are (1) Performance expectancy, (2) Effort expectancy, (3) Social influence and (4) Facilitating conditions regarding the names and descriptions in below table and moderated by four variables: gender, age, experience and voluntariness of use. External variables (1) Performance expectancy Description The degree to which an individual believes that using the system will help him or her to attain gains in job performance. (2) Effort expectancy The degree of ease associated with the use of the system. (3) Social influence (4) Facilitating conditions The degree to which an individual perceives that important others believe he or she should use the new system. The degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system. Table 1 Four cores of variables description ISS 465
In 2007, Wu and team are using UTAUT is used as the model to analysis the factors that significantly influenced the behavioral intention included Performance Expectancy, Social Influence, and Facilitating condition. The results can be helpful to Taiwan s mobile telecommunication companies to providing customer-oriented 3G services [5]. In 2013, Premsiri is using UTAUT to investigate the factors that influence behavioral intention of e-reader 400 users. The results show the performance expectancy, effort expectancy, social influence and new factor called perceived enjoyment are the highest level of usage factors. In future, should study the factors influencing to apply with their model [6]. III. MATERIALS AND METHODS We collected the theoretical framework of UTAUT to adjust in the model in Figure 2, addressed the features had obtained the statistical support. Otherwise, some non-assumed variables relationships included Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions and the last one is Features on web search engine are significant. Figure 2 Theoretical framework of UTAUT is adding the features. ISS 466
A. The hypotheses of this research were based on the theoretical of the UTAUT model: H1: The performance expectancy (PE) to the technology has a positive impact on users intention to adopt it. H2: The effort expectancy (EE) to the technology has a positive impact on users intention to adopt it. H3: The social influence (SI) to the technology has a positive impact on users intention to adopt it. H4: The facilitating conditions of a technology (FC) have a significant impact on users actual use. H5: The behavioral intention (BI) to adopt a technology has a significant impact on users actual use. H6: The features on web search engine (Features) to adopt a technology have a significant impact on users actual use. B. The Model Building Process (Lisrel programe) The researcher collected data from a survey of Internet users in Thailand publishing online through Google docs. Respondents participated in the number of 250 users, males 45% and female 55%. Processed for statistical proof LISREL program is the main structure analysis which has three phases, the first, building a model from literature review of researches and finding more information on the internet, the second stage is verification models from the model in the first with the actual data. In order to see what we have from the first stage model is consistent with the data in real situation. The final step of the modeling is model testing to confirm that the model collected is the actual situation. Figure 4 Showing the Model Building Process ISS 467
IV. RESULTS AND DISCUSSION Table 2 showing the descriptive statistics of respondents participated. (n=250) Variables Categories Percentage Gender Female 55% Male 45% Age (Year) 21-40 83% Over 40 17% Location (Currently live) Bangkok 70% Others 30% Approximately how long (years) have you been used Over 5 years 85% Until 1 to 5 years 13% Under 1 year 2% Average amount of time spent online web search engine. [Frequency] Every day (Always) 41% Once a day or more 28% 3-5 days a week 30% 1-2 days a week 1% The Internet devices PC - Windows 52% iphone OS 28% Others 20% Subject usability General 15% Maps or images 13% Places (Hotel / Travelling) 12% Translator 11% News 10% Entertainment/Multimedia 10% Shopping 9% Technology 8% Finance/Banking 6% Healthy /Medical) 4% Others 2% ISS 468
Table 3 showing rank web search engines results in Thailand Table 4 showing Cronbach Alpha Coefficient Cronbach Alpha Coefficient N of Items Performance Expectancy (PE) 0.769 3 Effort Expectancy (EE) 0.716 3 Social Influence (SI) 0.707 5 Facilitation Conditions (FC) 0.785 3 Features on web (F) 0.700 3 Behavior Intention (BI) 0.742 3 Use Behavior (USE) 0.718 4 V. CONCLUSION After modifying the UTAUT model frameworks, all fit indicators determinations have reached. According to Lisrel program building the model the structure consists of measurement model and structure equation model. Results of analysis can be explained for causal relationship, direct effects and indirect effect. In this study, we explore technology adoption factors of search engines influencing to users in Thailand. Result in the final ranking is still being Google. We hope that this study will be useful for web-engine developers who can be achieved in Thailand. ISS 469
REFERENCES [1] http://en.wikipedia.org/wiki/web_search_engine [2] Hans van der Heijden. (2002). Factors influencing the usage of websites: the case of a generic portal in The Netherland, Faculty of Economics and Business Administration, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands, Information & Management 40 (2003), 541-549 [3] Daniel E. Rose, Danny Levinson. (2004). Understanding User Goals in Web Search, in Proceeding of the WWW 04 Proceeding of the 13 th international conference on World Wide Web, 13-19 [4] Eugene Agichtein, Eric Brill, Susan Dumais. (2006) Improving Web Search Ranking by Incorporating User Behavior Information, in Proceeding of the SIGIR 06 proceedings of the 29 th annual international ACM SIGIR conference on Research and development in information retrieval, 3-10 [5] Yu-Lung Wu, Yu-Hui Tao, Pei-Chi Yang. (2007). Using UTAUT to explore the behavior of 3G mobile communication users. in Proceeding of the Industrial Engineering and Engineering Management, 199 203 [6] Premsiri Sangpoom. (2013). Analysis of factors influencing the acceptance of E-reader in Thailand, Faculty of Graduate studies, Mahidol University. [7] Venkatesh, V., Morris, M.G., Davis, G.B., and Davis, F.D. (2003). User acceptance of information technology: toward a unified view, MIS Quarterly27(3), 425-478. ISS 470