THE INFLUENcE OF TEcHNOLOGY READINESS ON BEHAVIORAL INTENTION (cASE STUDY OF ONLINE TRANSPORTATION IN INDONESIA AND THAILAND)

One of the popular technology-based services is online transportation. The company provides transportation services that can be accessed through an application on a smartphone. Therefore, the technology readiness of consumers needs to estimate the perception and behavior of the consumer. This research compared online transportation in Indonesia and Thailand by identifying the characteristics and technology readiness of online transportation consumers and the influence of technology readiness towards behavioral intention. This research was conducted in Indonesia and Thailand by a survey on 500 university students who were the online transportation consumers. The study used descriptive analysis and Partial Least Squares (PLS) analysis. Data processing used Mirosoft Excel 2010 software and SMART PLS. The results showed that consumer’s perception in Indonesia and Thailand was no different. In hypothesis test found that two accepted hypotheses, H1: optimism affected behavioral intention, and H5: favorableness affected behavioral intention.


INTRODUcTION
The development of internet-based technology in today's digital era provides convenience for consumers to get products and services they want. Technology became one of the things that are inseparable from humans. Its development then has an impact on the increasing number of internet users. According to the survey of Indonesian Internet Service Providers Association (APJII) in 2018, the number of internet users in Indonesia reached 143.26 million people, more than half of Indonesia's population that currently reach 262 million people. The development of technology on a country is supported by technological readiness of its people, or better known as the technology readines. As Parasuraman and Colby (2014) argues, technology readiness refers to people's propensity to embrace and use new technologies for accomplishing goals in home life and at work. There are four dimensions on technology readiness, namely, optimism (a positive view of technology and a belief that it offers people increased control, flexibility and efficiency in their lives), innovativeness (a tendency to be a technology pioneer and thought leader), discomfort (a perceived lack of control over technology and a feeling of being overwhelmed by it) and insecurity (distruct of technology and skepticism about its ability to work properly). Based on Table 1, Indonesia ranked 67 in the world and the 6th position in South-East Asia for tehnology readiness.
The importance of the technology readiness is due to the increasing number of digital techology-based services companies that have emerged to replace conventional companies. Consumer convenience in using the internet and meet their needs simply by using a smartphones makes digital techology-based services companies very well known and get high profits. The effect of technological development is the emergence of the service providers that provide service online (Lin et al. 2006), and that is why, one of the popular technology based service nowadays is online transportation. It has several unique differences compared to other online services because it has distinct services process, involves inseperable physical services (drivers and vehicle) and the user's tendency to use the services repeatedly (Silalahi et al. 2017). In Indonesia, online transportation that has a large market are Go-Jek and Grab, which made competition in this industry even tighter (Santoso and Nelloh, 2017;Aziah and Adawia 2018). These companies become the consumer favorite choice in meeting their transportation needs besides taxis, buses and other public transportations, especially in the cities in Indonesia which tend to experience congestion before and after office hours (Nurhidayah and Alkarim, 2017). In offering transportation services, Go-Jek and Grab also offers food delivery, courier, clean up, delivery groceries, truck service, massage service, woman skin care service, ticket booking service and busway monitoring service.
People tend to use online transportation to avoid congestion, which is a problem that often occurs in every country (Speranza, 2018), especially Indonesia. Therefore, companies of online transportation, should not be focused on the product only, but also market, competitors, users, suppliers to be identified real opportunities for innovation (Moroni et al. 2015). Some customers feel uncomfortable when confronted with self-service technology, because of the need for an understanding about the technology system used (Parasuraman, 2000), so it is important to identify this in determining the company's strategies. In making a decision to make an online purchase, consumers will make considerations such as searching for information about a product, considering other product alternatives, until finally deciding to make a purchase (Indrayana et al. 2016).
Therefore, by understanding the differences in people's technological readiness in Indonesia and Thailand, the companies need to look at existing market opportunities. The differences in the characteristic of Indonesia and Thailand communities can also be one of the things that affect their technology readiness to behavioral intention on online transportation use. Therefore, the objective of this research are: 1) To differentiate the consumer's characteristics of online transportation in Indonesia and Thailand; 2) To analyze technology readiness on the consumer online transportation in Indonesia and Thailand; 3) To analyze the influence of technology readiness to the behavioral intention on consumer online transportation in Indonesia and Thailand.
In addition, a moderating effect will be examinend to show the interaction between favorableness variables and independent variables in influencing the dependent variable (Zeithaml et al. 1996), i.e. H5 : Favorableness affects behavioral intention H6 : Favorableness moderates the relationship between optimism and behavioral intention H7 : Favorableness moderates the relationship between innovativeness and behavioral intention H8 : Favorableness moderates the relationship between discomfort and behavioral intention H9 : Favorableness moderates the relationship between insecurity and behavioral intention As illustrated in Figure 1, Online transportation is one example of companies which implement internet-based technology in delivering services. The development of this technology should also consider the technology readiness of its customers. According to The Economist Intelligence Unit (2018), Indonesia's position on technology readiness ranked sixth in Southeast Asia and Thailand were on the third place. However, even though Indonesia's position is lower than Thailand, the number of online transportation in Indonesia is quite high, which means many people knew and used online transportation. Therefore, this study aims to analyze the influence of technology readiness on behavioral intention.

characteristics of Respondents
The respondents used in this study were those who had used online transportation. Based on the results of data collection obtained through the distribution of questionnaires to 500 research respondents, it can be concluded that the respondents were dominated by female (as much as 80 percent) with the majority of respondents in the age range of 16-25 years. This is because the respondents are also dominated by students who are in the bachelor education level. This also caused the marriage status of the respondents to be dominated by singles. When viewed from the average total income, the majority of respondents had income in the range of Rp1,000,001 to Rp2,500,000 for Indonesia and less than 10,000 Baht (< Rp4,500,000) for Thailand. Meanwhile, for the average total expenditure, it is dominated by a

METHODS
This research was conducted in Indonesia and Thailand. Location selection is based on the background of the study. Primary and secondary data collection was conducted in February to April 2019. Types and sources of the data for this research are primary and secondary data. Primary data were taken through interview using questionnaire instrument to 250 students of Bogor Agricultural University and 250 students of Kasetsart University. Secondary data were taken from journals, books, previous research and internet.
The sampling method used in this study is classified as a non probability method. The non probability sampling technique used is convenience sampling with the total sample of 500 people consisting of 250 respondents from each country. The data analysis used in this study is quantitative and qualitative analysis. The analytical tool used is descriptive analysis and Partial Least Squares (PLS) analysis, to refine the test done in advance of the validity and reliability test. Data processing is done using Microsoft Excel 2010, SPSS and SMART PLS 3.
There are nine hypothesis in this study. Nugroho and Fajar (2017) stated that optimism and innovativeness have an effect on behavioral intention. Therefore, we infer that optimism and innovativeness will influence behavioral intention. H1: Optimism affects behavioral intention H2: Innovativeness affects behavioral intention Lin and Hsieh (2007) stated that technology readiness has an effect on behavioral intention. So, we hypothesize that: H3: Discomfort affects behavioral intention H4: Insecurity affects behavioral intention

Model Estimation
There are three stages in analyzing PLS, namely outer model analysis, inner model analysis and hypothesis testing. In the initial stage of the analysis, the outer model analysis will be carried out by looking at three indicators, namely convergent validity, discriminant validity and composite reliability. Next, an inner model analysis was performed using R-square and path coefficients for interconstructive significance tests in structural models. R-square is used to measure the level of variation in changes in the independent variable on the dependent variable. The path coefficient values indicate the level of significance in testing hypotheses performed by looking at the value of the T-statistic. The hypothesis is accepted when the T-statistic > 1.96 at alpha 5 percent (Abdillah and Jogiyanto, 2015;Ghozali, 2014).

The Influence of Technology Readiness to Behavioral Intention (Using Structural Equation Model (SEM) Analysis)
Structural Equation Model (SEM) is a multivariate analysis technique that is commonly used to construct and test statistical models that are usually shaped as a cause and effect model and in development are more powerful because they consider interaction modeling, nonlinearity, correlated independents, correlated error terms, measurement errors, latent multiple independents, each of which is measured using many indicators, and one or two latent dependent variables, each of which is also measured by several indicators (Sarwono, 2013). In this study, the SEM analysis used was SEM PLS with the help of SMART PLS 3 software.

Outer Model Analysis
This analysis is conducted to see the relationship between latent variables and their indicators. First, to see convergent validity, the coefficients of each indicator will be seen. Indicators that have a loading factor below 0.7 are considered inappropriate and must be removed from the model. However, according to Abdillah and Jogiyanto (2015), researchers should not delete indicators that have outer loading 0.5-0.7, as long as the AVE score and communality of the variable are more than 0.5. The results of initial and final convergent validity can be seen in Table 2.  To declare the reliability of the model, it will be tested by looking at the composite reliability (CR) and cronbach alpha. If the CR and cronbach's alpha values are greater than 0.7, the construct is declared reliable.
The values of AVE, CR and cronbach's alpha in the Indonesian model can be seen in Table 3 while for the Thai model can be seen in Table 4.
Based on Table 3 and Table 4 it is found that all the cronbach's alpha, CR and AVE values meet the criteria. Further explanation of the comparison between Indonesia and Thailand models can be seen in Table  5  After testing for convergent validity, discriminant validity is then used to analyze whether the indicator can reflect its latent variables by looking at the cross loading score. The value of cross loading construct correlation with the measurement item is expected to be greater than the size of other constructs. In addition, the measurement is continued by comparing the square  than 1.96. This stage is carried out by the bootstrapping method, which the final results can be seen in Figure 2 and Figure 3 for each country.
T-statistic test results and hypotheses can be seen in Table 6 (Indonesia) and

Structural Model Evaluation (Inner Model Analysis)
At this stage, the structural model is evaluated using R-square for the dependent construct and the value of the path or t-value coefficients for each path for the interconstructive significance test in the structural model. The R-square value is used to measure the level of variation in changes in the independent variables on the dependent variable (Abdillah and Jogiyanto, 2015).

Composite reliability (CR)
The value must be greater than 0.7 All of latent variables have composite reabilty above 0.7 (Table 3) All of latent variables have composite reabilty above 0.7 (Table 4) Cronbach's alpha The value must be greater than 0.7 All of latent variables have cronbach's alpha above 0.7 (Table 3) All of latent variables have cronbach's alpha above 0.7 (Table 4) Indicator reliability The value of loading factor is between 0.5-0.7 All of loading factor have value > 0.5 (Table 2) All of loading factor have value > 0.5 (Table 2) Avarage Variance Extracted (AVE) The value must be greater than 0.5 All of latent variables have AVE value > 0.5 (Table 3) All of latent variables have AVE value > 0.5 (Table 4) Discriminant validity of cross loading criteria The value of cross loading construct correlation with measurement items is greater than the size of other constructs All of cross loading have value greater than the size of other constructs (have qualified) All of cross loading have value greater than the size of other constructs (have qualified) Discriminant validity of Fornell-Larcker critetion.
The root of the AVE of a construct must be higher than the correlation between the latent variables All of AVE have root higher than the correlation between the latent variables (have qualified) All of AVE have root higher than the correlation between the latent variables (have qualified) variable does not have a significant effect on someone's behavioral intention to use online transportation (H2 is rejected). This is supported by the research of Septiani et al. (2017), where innovativeness has no effect on behavioral intention, which can be caused by high consumer consumptive but low innovative mind. Another reason is that indicators of innovativeness reflect that one can use or buy the latest available technology, which also relates to the income of that person. The latest technology has a fairly expensive price and if someone is always a pioneer in its use, of course the person has a large enough income to buy it. Whereas in this study, respondents were dominated by students whose income was dominated in the range of Rp1,000,001 to Rp2,500,000 for Indonesia and less than 10,000 Baht for Thailand. That is why innovativeness variables have no influence on behavioral intention in both Indonesia and Thailand. The use of online transportation and consumer loyalty in using it is not influenced by how consumers can buy or use the most recent technology.
In addition, based on Table 6 and Table 7, it is known that the optimism variable has a positive and significant effect on behavioral intention (H1 accepted). This means that the higher the level of optimism a person has, the higher the behavioral intention towards using online transportation. This is supported by the research of Lin and Hsieh (2007) which states that technology readiness has a positive effect on behavioral intention. Someone who has a positive view and high trust in technology tends to have a high level of loyalty towards him. Therefor it can be concluded that the level of loyalty in Indonesia and Thailand towards online transportation is influenced by the used belief in the technology itself.
However, in this study both in Indonesia and Thailand, the variables of innovativeness, discomfort and insecurity did not have a significant effect on behavioral intention. The tendency of someone to be a pioneer in using technology does not necessarily make him loyal to a technology. Therefore, the innovativeness

cONcLUSIONS AND REcOMMENDATIONS conclusions
Respondents in this study were dominated by female as much as 80 percent (75.60 percent in Indonesia and 84.40 percent in Thailand) with the majority of respondents in the age range of 16-25 years. This also caused the marriage status of the respondents to be dominated by singles. According to the average total income, the majority of respondents had income in the range of Rp1,000,001 to Rp2,500,000 for Indonesia and less than 10,000 Baht (< Rp4,500, 000) for Thailand. Meanwhile, for the average total expenditure, it is dominated by a range of Rp500,001 to Rp250,000 for Indonesia and 5,000-10,000 Baht (Rp250,000 -Rp4,500,000) for Thailand.
The results of the hypothesis test for these two countries are also no different. In hypothesis testing in Indonesia and Thailand, it was found that there were only two accepted hypotheses, H1 which stated that optimism had an effect on behavioral intention and H5 which stated that favorableness had an effect on behavioral intention. However, favorableness as a moderating variable does not mediate the relationship between each independent variable to the dependent variable.
From the results of this study, it can be concluded that there are similarities between the people of Indonesia and Thailand in their technological readiness and their behavioral intention to use online transportation. Therefore, online transportation companies can develop strategies that are not much different for these two countries.

Recommendations
Based on the results of this study, it is suggested that online transportation companies review the strategies that have been implemented so far, and focus on improving the quality of services to improve convenience for consumers in accessing services. In addition, pricing is important to be able to compete with other public transportation and conventional taxi. Furthermore, it is necessary to develop deeper and broader research such as making comparisons with other countries, especially those outside of Asia that have online transport policies or policies that are far different from Indonesia, so that online transportation companies in Indonesia can also learn from outside countries and can expand the market. In addition, Variable discomfort and insecurity also do not have a significant effect on behavioral intention (H3 and H4 rejected). This is because these two variables are inhibitor variables of technology readiness that show a person's negative side in using technology. This is also supported by research from Nugroho and Fajar (2017) which shows that the variables of discomfort and insecurity do not affect perceived ease of use and perceived usefulness, which are behavioral variables that form the technology acceptance model.

Managerial Implications
Consumers use online transportation through applications, therefor the high level of smartphone users in Indonesia and Thailand can also influence the results of hypotheses 3 and 4. According to the results of a survey from Google and Temasek entitled 'e-Conomy SEA 2018', smartphone users in Indonesia reach 60 percent of the total population and Thailand with 70 percent of the total population. Thus, even though it was found that the level of discomfort and insecurity from Indonesian and Thai respondents are still high, this is not a major factor affecting their loyalty using online transportation. Therefore, based on this study it is found that both in Indonesia and Thailand, the level of discomfort and insecurity do not affect a person's behavioral intention to use online transportation.
Therefore, from this result, the companies of online transportation should make the right strategies, such as pricing and quality service. In Thailand, people tend to use public transportation such as buses, songtaew, MRT, BTS and others because the prices offered by online transportation tend to be more expensive (Taneerananon, 2016). Meanwhile, in Indonesia pricing strategy also important because the pricing on online transportation is determined solely by calculating the distance and also the road congestion (Wahyuningtyas, 2016). Companies also should improve their quality service, because according to Damaini et al. (2018) there are a number of problems that occur quite frequently in online transportation and make it difficult for passangers and drivers, namely, troubled GPS network, bad ratings and comments given by passangers after using services, cancellation of orders resulting in financial loss, buy and sell driver account and fraud both from the driver and the passenger.