Available online at www.globalilluminators.org GlobalIlluminators Full Paper Proceeding GTAR-2014, Vol. 1, 259-268 FULL PAPER PROCEEDING Multidisciplinary Studies ISBN: 978-969-9948-30-5 GTAR-14 Measurement for Analyzing Instant Messenger Application Adoption Using Unified Theory of Acceptance and Use of Technology 2 (utaut2) Gusti Ayu Made Mas Marhaeni 1* and Indrawati 2 Faculty of Economics and Business, Telkom University of Indonesia Abstract The average of customers in using instant messenger applications such as WhatsApp, Blackberry Messenger (BBM), LINE, KakaoTalk, and WeChat in Indonesia is 40 minutes per customer per day with the number of application downloaded in Indonesia is the second highest among six countries in South East Asia. This data is contradictive with frequency of usage application in Indonesia which is the lowest among other South East Asia countries. This highest number of downloaded application which is not in line with highest usage of application is not good for the business. It is important to know the factors that affect the intention and usage of instant messenger application. So far, there is no well published research regarding this matter, yet. This research wants to propose a measurement model to analyze factors that affect the behavioral intention and usage of instant messenger applications in Indonesia, especially in Bandung based on UTAUT 2 model from Venkatesh, Thong, and Xu (2012). The measurement model has been tested by using 30 respondents, the users of instant messenger applications in Bandung. The pilot test reveals that the measurement model is valid and reliable to be used for further study. 2014 The Authors. Published by Global Illuminators. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Scientific & Review committee of GTAR-2014. Keywords Measurement, Instant Messenger Application, Adoption, UTAUT2. Introduction The availability of website for an institution is very important, especially for an educational institution just like ABC University. The website can become a means of communication from the institute to its stake holder: students, parents, industries, lecturers, administrators, and government or from the stakeholders to the institution. Website can also become one of important tools to advertise the institution. Through its website the institution can describe its services to the prospective customers from all over the world. The information in website can be in a form of written materials, audio, and video. This will make the information clearer rather than communication through off line advertising, such as leaflet, billboard, newspaper, magazine, or even radio and television. Since website can be said no limit and can be access when ever the prospective customers want, 24 hours a day 7 *All correspondence related to this article should be directed to Gusti Ayu Made Mas Marhaeni, Faculty of Economics and Business, Telkom University of Indonesia Email: marhaeni@students.telkomuniversity.ac.id 2015 The Authors. Published by Global Illuminators. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Scientific & Review committee of GTAR-2015.
days a week. The website is relatively easy to access, the prospective customer just click on the address and all the information needed will be available in front of him/her. The customer can read the written information and play the video or audio as long as they want. The website can also become user content generation which enables users to create their own content and can be used interactively between the users and the providers (Laudon and Traver, 2009). It is no doubt if a website will make the information about the education institution clear and understandable. Website can make the communication become effective (Hoo, Kueh, and Zawawi, 2009). Website can also become a tool for lecturers to disseminate and spread their achievement, for example to disseminate their research results or books which of course will make the image of the lecturers increase, and more important to make use of their achievements in the real live and in more broad area rather than only talked in front of the students in the class. The availability of website is also important to increase the image of the institution, since there is university web ranking published by WeboMatrix annually. Webomatrix ranks education institution based on some criteria, one of the criteria is the frequency of people visiting its website. The highest the number of people visit the website the highest the score of education institution will be. ABC University has already built its website and communicates, socialize as well the availability of its website to all students, lecturers, and administrator. So far the adoption of the websites by students, lecturers, and administrators especially by using the Modified Unified Theory of Acceptance and Use of Technology (UTAUT) has not been explored. This lack of knowledge motivated the researcher to conduct this study. This study investigates the key and moderating factors that affect the behavioural intention and used behavior of customers toward the use of the website in ABC University Indonesia. The approach employed for this study is based on a modified unified theory of acceptance and use of technology (UTAUT) model (Indrawati, Raman, and Chew 2010, Indrawati, Murugesan, and Raman 2010a, b). Problem Statements Based on the above discussion, the problem statements are as follows: 1) Website offers higher value or more benefits than other media such as newspaper, magazine, radio, and television. Website has been built and implemented in ABC University, and ABC University has made significant investments in building website. However, service has not been searched if it is widely adopted by students, lecturers, and employee of ABC University. 2) The key adoption factors that affect the behavioral intention and use behavior of ABC University s students, lecturers and administrators toward using website are not yet well understood. Research Questions and Objectives Given the problem statements above, the present study seeks to answer the following research question: Based on the modified UTAUT model, which factors affect the behavioral intention of students, lecturers, and administrators toward using website? The objective of this study is to test the key factors from the modified UTAUT model that affect the behavioral intention of students, lecturers, and administrators toward the use of website in ABC University Indonesia. 260
Scope of the Study This study covers the behavioral intention and used behavior of students, lecturers and administrators of ABC University Indonesia. This study uses a sample from the existing students, lecturers, and administrators of ABC University Indonesia. As an education institution which is founded by Telkom Indonesia Foundation, ABC University should be able to understand their students, lecturers, and administrators behavior, specifically the factors affecting their intention for the website adoption, and design services based on their needs to improve their willingness to adopt the website. Literature Review and Conceptual Model The conceptual model of this study is a modified theory of the Unified Theory of Acceptance and Use of Technology (UTAUT) founded by Indrawati (2012). Indrawati modified UTAUT model of Venkatesh, Morris, Davis, and Davis (2003). UTAUT is one of the more recent models of technology adoption. The UTAUT model has the highest explanatory power in predicting the behavioural intention of consumers to use an information technology (IT) (Venkatesh et al., 2003; Wu et al., 2008). This argument is one of the reasons why UTAUT was selected as the basis for the theoretical framework of Indrawati s work at 2012. The original UTAUT model developed by Venkatesh et al. (2003) had the following four constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions (Venkatesh et al., 2003; Wu et al., 2008). However, based on a previous study in 3G Mobile multimedia services, this model was found to be unsuitable for application in an Indonesian context. Therefore, the UTAUT model used in this study has been modified through the inclusion of content, as an additional construct that should be added to the original UTAUT model (Indrawati, 2012). This present study tests if the modified UTAUT model of Indrawati (2012) can be used to predict the behavioural intention of customers to use website in a university in Indonesia. Figure 1 shows the schematic relationship among constructs used in this study. Performance Expectancy Effort Expectancy Social Behavioral Intention Facilitating Content Figure 1: Conceptual Model for Behavioural Intention toward the Use of Website Based on the Figure 1 Conceptual Model for Behavioural Intention toward the Use of Website, there are 5 dependent variables which directly influence the behavioural intention 261
of customer to use website, namely performance expectancy, effort expectancy, social influence, facilitating conditions, and content. Performance expectancy in this study is defined as the degree to which a person believes that using website will enhance fast access to information and communications that can be used to accomplish tasks at any time and place, and to get enjoyment (Indrawati, 2012; Carlsson et al. 2006; Dwivedi & Irani, 2009; Pagani, 2004; Venkatesh et al., 2003). Effort expectancy in present study is defined as the degree to which a person believes that accessing website will be easy (Davis, 1989; Indrawati, 2012; Venkatesh et al. 2003). Social Influence is defined as the degree to which an individual senses that a person who is important to him or her thinks that he or she should use website (Ajzen, 1991; Davis et al., 1989; Fishbein & Ajzen, 1975; Indrawati, 2012; Venkatesh et al., 2003). Facilitating condition is defined as the degree to which an individual believes that factors such as coverage, network, and availability of devices exist to support the use of website and make the activities easy to accomplish (Thompson et al., 1991; Venkatesh et al., 2003). Content is defined as material prepared by professionals or community to be used by a large number of people, distributed and accessed through website (Daud, 2008; Hoo, Kueh, & Zawawi, 2009; Indrawati, 2012; Odlyzko, 2001). Behavioural is defined as the degree to which a person will use website in the future (Glassberg, 2000; Indrawati, 2012; Venkatesh et al., 2003). Hypotheses and Measurement In line with the definition of each variable and the schematic relationship among variables as shown in Figure 1, the hypotheses of this present study are as follows: H.1. Performance expectancy has a positive influence on behavioral intention. H.2. Effort expectancy has a positive influence on behavioral intention. H.3. Social influence has a positive influence on behavioral intention. H.4. Facilitating conditions has a positive influence on behavioral intention. H.5. Content has a positive influence on behavioral intention. To test the hypotheses this study collected data by using questionnaire. The validity and reliability of the questionnaire were assessed to ensure that the questionnaire accurately measured the constructs (Hair, Black, Babin, & Anderson, 2010; Sekaran & Bougie, 2010). The reliability test determines the consistency of a developed instrument in measuring its target construct, and can be conducted using an inter-item consistency reliability test (Sekaran & Bougie, 2010). Among the types of this test, the most popular for multipointscaled items is the Cronbach s alpha (CA) coefficient; the higher the coefficient, the better the measuring instrument. CA coefficients with values ranging from 0.60 to 0.70 are deemed as the lower limit of acceptability; thus, a minimum value of 0.7 is necessary to indicate reliability (Hair et al., 2010). The composite reliability (CR) can similarly measure construct reliability and required a minimum CR value of 0.7. Validity is verified to determine the effectiveness of the developed instrument in measuring its target construct and whether the instrument measures the correct construct (Sekaran & Bougie, 2010). Validity can be divided into: (a) content or logical validity; (b) criterion-related validity, which is categorized into predictive and concurrent validity; and (c) construct validity, which can be measured by convergent and discriminate validity. Content, logical, or face validity assesses the degree to which the measure covers an adequate and representative set of items that tap the concept; the higher the representation of the scale items for the measured concept, the greater the content validity (Sekaran & Bougie, 262
2010). Content validity is sometimes called face validity because the assessment considers the measure to ascertain the sampled domain. If the measured items look different from the possible domain, the measures lack content validity (Churchill & Iacobucci, 2005). Content validity can be achieved through careful item selection, a studious review of previously tested measures, as well as through incorporation of suggestions from literature and information obtained from a pilot study of the instrument (Chew, 2007; Hair et al., 2010). All of these suggested methods for achieving good content validity of a construct were used in preparing the operationalization for each construct in the present study. The questionnaire was also assessed by one education expert who also as an expert in the Indonesian language. The aim of this process was to validate the content of the questionnaire in terms of relevance, representativeness, suitability, accuracy, and wording. Construct validity determines how well the results obtained from the measurement fit with the theories around which the test was designed (Sekaran & Bougie 2003). Furthermore, Sekaran (2003) and Sekaran & Bougie (2010) explained that construct validity can be assessed through convergent. The convergent validity can be measured by using factor loadings (FL) (Hair et al.). The FL of 0.7 or higher indicates good convergent validity (Chin & Dibbern, 2010; Henseler et al., 2009; Urbach & Ahlemann, 2010). To test the validity and reliability of the questionnaire used, this study the distributed the questionnaire to 48 respondents. The collected data were analyzed by using 18 SPSS software. There are 39 items of 6 constructs, 2 items are not valid so they are deleted from the questionnaire, and 37 items are valid and retained in the questionnaire that distributed to the respondents. Data Collection Method A survey method using a questionnaire was conducted to collect the main data for testing the hypotheses. A population is the aggregate of all the elements that share a common set of characteristics and comprises the universe for the purpose of the research problem. A target population is the collection of elements or objects that possess the information sought by the researcher and about which inferences are to be drawn (Malhotra, 1999). Following these definitions, the population of this study comprises students, lecturers, administrators, and management of ABC University. The sample size in the present study will be based on the sample sizes in previous studies that were published in international journals and/or based on previous researches in the marketing field. Based on a consideration of the sample sizes in such journals and in marketing studies such as problem identification research, this study involved 318 respondents which are selected by using purposive sampling. The purposive sampling technique is selected when the sampling is confined to specific types of people who can provide the desired information, either because they are the only ones who have it or because they conform to some criteria set by the researcher (Sekaran, 2003). In this study, the criteria are people who knows the address of IMT website and has ever used it. Data were collected from November 1-29, 2012 through a survey of 321 respondents from the eight areas covered by this research. Out of the 318 questionnaires, 8 were discarded because three or more items in those questionnaires were not answered, or all the questions had the same answers. A further 13 questionnaires were discarded because the respondents stated that they did not know the address of IMT website or never use IMT Website. Thus, the total number of questionnaires for data analysis was 310. 263
Data Analysis Method and Result The 310 collected data were analyzed using the quantitative investigation method. The quantitative investigation was used for its appropriateness to model testing (Chew, 2007), the results of which were confirmed by the qualitative investigation. The quantitative analysis method used in the study was the PLS method, which is a variance-based technique of structural equation modeling (SEM). The PLS method can analyze structural models with multiple-item constructs, as well as measure direct and indirect paths. PLS can also produce standardized regression coefficients between constructs as well as loadings between items and constructs (similar to principal components analysis). Values for dependent constructs are similarly produced. This study chose SmartPLS 2.0 M3, a PLS software packages, which was developed by Ringle, Wende, Will (2005) of the Institute of Operations Management and Organizations at the University of Hamburg, Germany. This software can be downloaded for free at http://www.smartpls.de.the reasons for the selection of PLS are a. PLS has been used more frequently than covariance-based approaches such as Amos or Lisrel in IS research (Urbach & Ahlemann, 2010); b. PLS has been commonly used in UTAUT studies; c. PLS places fewer demands on the underlying data distribution; d. PLS places fewer demands on the measurement scales of the manifest indicator; e. PLS can analyze data from small (38 samples) to large sample sizes (1000 or more samples); and f. PLS is primarily used for prediction (explained variances) and exploratory work (Hair et al., 2006; Ringle et al.. 2012; Urbach and Ahlemann, 2010). Such focus is in line with the objective of the present study to predict the influence of the five key factors on the behavioural intention of consumers to use Website. Analysis using PLS involves two steps: (1) assessment of the measurement model to test the reliability and validity of the instrument; and (2) assessment of the structural model to test the research hypotheses (Chin & Dibbern, 2010; Henseler et al., 2009; Urbach & Ahlemann, 2010). Assessment of the measurement model is generally performed to ensure that the measurement is reliable and valid before making any conclusions about the relationships between the constructs of the model. The measurement model can be tested by evaluating indicator reliability shown by FL value that should be at least 0.7; internal consistency reliability shown by Cronbach Alfa (CA) and Composite Reliability value should be at least 0.7, convergent validity measured by Average Variance Extracted (AVE) that should be at least 0.5 (Henseler et al., 2009; Urbach and Ahlemann, 2010). Data collected from 310 respondents are processed by running SmartPLS software. The results shows that there are 13 items which have FL less than 0.7 (the requirement for more advanced study). Those items are deleted from the model. The new model are now consists of 24 items processed again by using SmartPLS software. The results are shown by Figure 1 below, that all the items fulfilled the validity requirement. Table 1 shows the CA, CR, and AVE values of all constructs that fulfilled all the requirements. Thus, the measurement model of this study is valid and reliable. Table 1: The CA, CR, and AVE Values of Each Construct Construct CA CR AVE Construct CA CR AVE Content 0.7 0.8 0.6 Intention 0.8 0.8 0.5 Effort 0.8 0.9 0.6 Performance 0.8 0.9 0.7 Facilitating 0.8 0.9 0.6 Social 0.8 0.9 0.6 264
In PLS, the correctness of the proposed model can be measured by using Path coefficient (PC), R-squared (R²). The path coefficients should have t-values of at least 1.96 to be considered significant at the 95% confidence level (Chin & Dibbern, 2010; Henseler et al., 2009; Urbach & Ahlemann, 2010). The t-values are then obtained using re-sampling techniques, such as bootstrapping (Ringle et al., 2005; Urbach & Ahlemann, 2010). Table 2 show the path coefficients and t-values of the model as a result of bootstrapping. Table 2: The Path Coefficients and t-values of the Model Paths Path Coefficients t-values Content -> Intention 0.38 4.54* Performance -> Intention 0.2 2.61* Facilitating -> Intention 0.16 1.79** Sosial -> Intention 0.18 1.6** Effort -> Intention 0.12 1.56*** Note: * represents significance at the 99% confidence level. ** represents significance at the 95% confidence level *** represents significance at the 90% confidence level All the paths are significant, this means that all the hypotheses in this study are supported by the data. Based on the SmartPLS, which can directly produce FLs and path coefficients as well as R² by clicking on Calculation for PLS Algorithm, the model of this study achieved an R² of 0.616, as shown in Figure 2 below. This result indicates that the model substantially explained the variance in the behavioural intention of customers to use Website. Figure 1: The SmartPLS Result of the Model 265
Conclusion and Discussion All the hypotheses in this study are supported by the data. The respondents of this study believe that content is the most influential factors that effect their behavioral intention to use website. They consider the content of Website more than other construct when they wish to use this service. If they find that the content of Website is as good as or is better than expected, they are more likely to have the intention to use Website. This finding is in line with the research result of Indrawati (2012). The second most important is performance expectancy, the respondents of this study believe that performance expectancy is one of key factors influencing their behavioral intention to Website, which is in line with the results of Dapper (2007), Stephan, Patrick, Koen, and Ed (2009), Venkatesh et al. (2003), and Indrawati (2012). The third and fourth factors are facilitating condition and social influence. The last factor is effort expectancy of using Website. Effort expectancy is positively influenced the behavioural intention of the respondents to use Website, indicating that the respondents believe that effort expectancy is one of key factors influencing their behavioural intention to use the service. Contribution of This Study The results of this study can be of great value in the development of a theory regarding the behavioural intention of prospective consumers to use Website using a modified UTAUT model. Content was confirmed as an independent variable that should be added to the original UTAUT model proposed by Venkatesh et al. (2003) as has also been confirmed by Indrawati (2012). Such addition is still rare. Thus, this study fills a gap in the literature technology adoption using UTAUT model. Moreover, the findings of this study regarding adoption issues on Website are significant from the perspective of business practice. The results of this study are hoped to be of great use to ABC University in its development of policies to enhance the adoption of Website and to eventually increase the numbers and durations of using Website. References Ajzen, I. (1991).The theory of planned behavior. Organizational Behavior and Human Decision Process, 50 [Online]. Available: http://www.courses.umass.edu/psyc661/pdf/tpb.obhdp.pdf [2009, February 25]. Chew, K.W. (2007). Modified Technology Acceptance Models as a Predictor of Online Purchase Behavior : a Malaysian Perspective (Doctoral Dissertation, Multimedia University, July 2007) Chin, W.W., and Dibbern, J. (2010). An Introduction to a Permutation Based Procedure for Multi-Group PLS Analysis: Results of Test of Differences on Simulated Data and a Cross Cultural Analysis of the Sourcing of Information System Services Between Germany and USA. In V.E. Vinzi, W.W. Chin, J. Henseler, H. Wang (Eds.), Hanbook of Partial Least Squares Concepts, Methods, and Application. (pp. 171 193). Berlin: Springer Churchil, G.A., and Iacobucci, D. (2005). Marketing Research Methodological Foundations. Ninth Edition. South-Western: Thomson Corporation. Dapper, G. (2007) User acceptance of Enterprise 2.0 A case study at an internationally operating private bank [Online]. Available: http://www.google.co.id/#hl =id&q=utaut+model &start=90&sa=n&fp=983862b504061180 [2009, April 9]. 266
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