The Influence of Perceived Usefulness, …irceb.org/jurnal/2017/hr/125 ANTONIUS SIAHAAN.docx · Web...

The Influence of Perceived Usefulness, Perceived Ease of Use, and Cultural Dimensions towards Actual Use of E-Commerce in Indonesia

2017

The Influence Perceived Usefulness, Perceived Ease of Use, and Cultural Dimensions towards Actual Use of E-Commerce in Indonesia

Novia Purna [email protected]

Dr. Antonius TP. Siahaan, SE., Akt., [email protected]

Parhimpunan Simatupang, SE., [email protected]

Abstract

In a globalization stage, e-commerce has a lot of devotees and becomes the innovation strategy for company gain marketing. The interesting part is, not merely that country has the similar behavior acceptance to e-commerce. This research examines about the influence of perceived usefulness, perceived ease of use, cultural dimensions (power distance, individualism, masculinity, uncertainty avoidance, long-term orientation, indulgence) towards actual use of e-commerce, specifically in Indonesia. The culture dimension is following Hofstede and Minkov theory. The technology acceptance model is following Davis and Venkantesh theory. Perceived usefulness, perceived ease of use, power distance, individualism, masculinity, uncertainty avoidance, long-term orientation becomes independent variable. Intention to use becomes intervening to relate independent variable to dependent variable, in this research is actual use. The data were collected to 410 respondents using online and offline method deployment. The respondents are coming from Bandung, Medan, Jakarta, Makassar, Pontianak, and Bali. In which, those city is selected by the high percentage of internet user, population,

and also capital city. As for analytic data, partial least square – structural equation modeling was used for test hypothesis. The results indicates that perceived usefulness, perceived ease of use, power distance, and masculinity influence intention to use and intention to use influence actual use of e-commerce. Other variables such as individualism show positive influence. As for uncertainty avoidance, long-term orientation, and indulgence shows insignificant influence result to intention to use e-commerce. These results add to research on how perceived usefulness, perceived ease of use can be considering the e-commerce practices by marketing strategy. Also, the strategy can be considered about culture in Indonesia in which power distance and masculinity shows negatively significant to e-commerce.

1. Introduction

According to Saroja (2012), today’s retailer’s faces some of the toughest issues ever experienced in the history of the industry from a fundamental change in the way consumers shop to greatly increased expectations for service and price.

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Information technology plays a vital role in how well the customers are satisfied to achieve revolutionary excellence in marketing, customer service, and associate effectiveness and supply chain efficiency. Electronic Commerce or known as e-commerce becomes a lot of devotees. Considering large population, Indonesia will be a huge market potential (See figure 1.1).

Figure 1.1 Asian’s Market Potential

Based on Asosiasi Penyelanggara Jasa Internet Indonesia (APJII) in November 2016, Indonesia has 256.200.000 populations, 132.700.000 internet user. (See figure 1.2).

Figure 1.2 Internet User Penetrations in Indonesia

Regarding the e-commerce, in 2017, there are 258 E-commerce Organizer which registered in Ministry of Communication and Information Technology in Indonesia.

The survey result also from APJII shows the amount of behavior of internet user in Indonesia is 84.2 million who do the transactions in online.

Figure 1.3 Internet Users Behavior in Indonesia

As outlined by Ministry of Communication and Information Technology (2012), one of the factors that can support activity of e-commerce is tendency of changing in people behavior. Behavior of people to accept the technology can be influenced by perception and national culture, since every country shows it differently. Especially in developing country such as Indonesia, it depends on globalization trends.

Technology Acceptance Model (TAM) has been used extensively in the existing literature to explain and predict consumer behavior in an online environment (Pavlou, 2003). The role of culture has a great impact on the diffusion patterns of technology across different countries (Da Silviera, Borenstei, & Fogliatto, 2001; Elliot, 2002; Eastin, 2002; Sundqvist, Frank, & Puumalainen, 2005). Hoftsede analysis is very suitable to analyze national-culture. Since the research executed 50 countries and 3 regions on the same attitude survey in different countries (Hoftstede, 2010).

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One of e-commerce company that in the top list is Lazada (ecommerceiq.asia, 2017). Lazada understands about the customer in Indonesia, they create Cash on Delivery (CoD). It is one of the example how marketplace can understand the culture of a country and also the perception of people. Since Indonesia culture still conservative, in which customer prefer select a comfortable become the first option. The system business in Lazada is B2C or Business to Customer.The percentage between internet users who only have knowledge about e-commerce is 130.8 million and internet buyer is 84.2 million. The gap is 46.6 million people.

This paper objectives is to know whether perceived usefulness, perceived ease of use, power distance, individualism, masculinity, uncertainty avoidance, long-term orientation, indulgence are influencing positively or negatively towards the intervening variable- intention to use. After that, analyze the influence of intention to use to actual use of e-commerce. 2. Literature Review and Hypothesis

This part provides theoretical foundations of the study which are relevant in conducting the research.

2.1 Perceived Usefulness

Davis (1986) introduce the original model of Technology Acceptance Model (TAM) and currently Davis and Venkantesh et.al. (2000) has an extension of the model known as TAM2. Wibowo (2008) explains that perceived usefulness is a measure by which the use of a technology is believed to be bringing benefits to the people who use them.

According to TAM, one’s actual use of a technology system is influenced directly or indirectly by the user’s behavioral intentions, attitude, perceived usefulness of the system, and perceived ease of the system.

Hypothesis 1: Perceived usefulness (X1) positively significant influence towards intention to use (Z).

2.2 Perceived Ease of Use

Previously, Davis (1988) Perceived Ease of Use (EOU) refers to the degree to which prospective user expects the target system to be free of effort. Kusuma and Susilowati (2007) argue that the intention of use and interaction between users with the system can also be shows ease of use. A frequently used system shows that the system is better known, easier to operate, and easier to use by its users.

Hypothesis 2: Perceived ease of use (X2) positively significant towards intention to use (Z).

2.3 Cultural Dimensions

According to Peter, J. Paul and Jerry C Olson (2005), cultural meanings include common affective reactions, typical cognitions (beliefs), and characteristics patterns of behavior. Hofstede developed his theory of culture 30 years ago and is the most widely cited (Jones, 2007). Although the research is conducted many years ago, the theory remains valuable due to the slow change of culture (Taras et al., 2009).

Professor Geert Hofstede conducted one of the most comprehensive studies of how

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values in the workplace are influenced by culture. The six dimensions of national culture are based on extensive research done by Professor Geert Hofstede, Gert Jan Hofstede, Michael Minkov and their research teams in 2010.

2.3.1 Power Distance

This dimension expresses the degree to which the less powerful members of a society accept and expect that power is distributed unequally. Describes the extent to which people accept that power is distributed unequally within a group or a society (Taras et al., 2009). Higher power distance often implies the reliance on formal rules and the existence of hierarchical and centralized decision structures (Erumban and de Jong, 2006).

Individuals in higher power-distance settings are less likely to experiment with new technologies. This is because such technologies do not have a precedent and are less likely to be mandated by superiors, and therefore such experimentation would require significant autonomous decision-making. Therefore groups with lower power distance would naturally be more conducive to adopting new technologies. (Zakour, 2004).

Hypothesis 3: Power distance (X3) negatively significant influence towards intention to use (Z).

2.3.2 Individualism

The high side of this dimension, called individualism, can be defined as a preference for a loosely-knit social

framework in which individuals are expected to take care of only themselves and their immediate families.

In technology adoption in particular, people with individualist inclinations tend to focus on their own goals and satisfaction, rather than those of the group or society (Lee et al., 2007). Therefore higher individualism is associated with greater rates of adoption of new technologies (Van Everdingen and Waarts, 2003).

Hypothesis 4: Individualism (X4) positively significant influence towards intention to use (Z).

2.3.3 Masculinity

The Masculinity side of this dimension represents a preference in society for achievement, heroism, assertiveness and material rewards for success. Because effort-free use is more concerned with creation of a pleasant and less frustrating work environment, and quality of work life concerns are typically feminine values, the lower the degree of MAS, the higher the effect of PEOU on IT adoption.

Hypothesis 5: Masculinity (X5) negatively significant influence towards intention to use (Z).

2.3.4 Uncertainty Avoidance

The Uncertainty Avoidance dimension expresses the degree to which the members of a society feel uncomfortable with uncertainty and ambiguity. Individuals with high uncertainty

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avoidance are likely to feel uncomfortable in uncertain situations and be threatened by ambiguity (Erumban and de Jong, 2006; Yoon, 2009).

Since adoption of new technology often involves high degrees of ambiguity, individuals with higher uncertainty avoidance may be less likely to adopt new technologies (Zakour, 2004).

Hypothesis 6: Uncertainty avoidance (X6) negatively significant influence towards intention to use (Z).

2.3.5 Long-Term Orientation

Every society has to maintain some links with its own past while dealing with the challenges of the present and the future. Societies prioritize these two existential goals differently.

Generally, trust recognizes risk, such as vulnerability and/or uncertainty, about an outcome. People of a high LTO culture have strong beliefs that allow them to take risk during uncertainty (Yoon, 2009).

Hypothesis 7: Long-term orientation (X7) negatively significant influence towards intention to use (Z).

2.3.6 Indulgence

Indulgence stands for a society that allows relatively free gratification of basic and natural human drives related to enjoying life and having fun. Optimism plays a powerful role and strengthens trust in online environments (Uslaner,

1998; Coppola et al., 2001; Lumsden and MacKay, 2006).

Online trust and social trust is highly effective in developing positive attitudes and enhancing the willingness towards online shopping (Gefen, 2000; Tan and Thoen, 2002; Dahlberg et al., 2003). In other words more indulgent societies are willing to shop online more.

Hypothesis 8: Indulgence (X8) positively significant influence towards intention to use (Z).

2.4 Intention to Use

A wide variety of researches have shown the intention to use a technology has been the strongest predictor of actual usage behavior (Davis & Venkatesh, 2004; Venkatesh & Davis, 2000).

Hypothesis 9: Intention to use (Z) positively significant influence towards actual use (Y).

3. Research Methodology

3.1 Data Collection

In order to test the hypotheses, data was collected from 6 regions in Indonesia, such as Bandung, Medan, Jakarta, Makassar, Pontianak, and Bali. Indonesia has 256.200.000 populations and 132.700.000 internet user. Questionnaire method is used in this research. The measurement is representing in table 2.1. Sample has used in this research. Based on slovin calculation, the sample should be 400 respondents, however the return responses is 410. Hence, all the responses are using in this research.

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The test that will be used in this research is pre-test and post-test. Pre-test has been populated to small of sample respondents prior the real questionnaire to sample. The purpose of this pre-test is to test the questionnaire before a full-scale research, in order to identify problems such unclear wording or the questionnaire has many steps of administer. The method of the pre-test is validity and reliability test. The result of pre-test shows that 2 (two) indicators should be discarded, that is 4th questions from Uncertainty Avoidance and 4th questions from Long-Term Orientations. Sum is 38 questions should be populated through post-test.

These variables were included in the sample to achieve representativeness and control for distribution of responses across specific domicile, age, gender, occupation, education level, monthly income (rupiah), marital status, source of learning e-commerce, monthly expenses. In total, 410 respondents are returned. 53% from sample or 217 respondents were female and 47% from sample or 193 were male. 250 respondents were agreeing to use e-commerce 6 – 8 times per month. More than 50% are learning e-commerce on the fly. Detailed descriptive statistics on the respondents’ characteristics are shown in table 2.1.

Table 2.1Descriptive Statistics of Respondents

Measure Value Frequ-ency

Percen-tage

Domicile Bandung 214 52%Medan 65 16%Jakarta 48 12%Makassar 40 10%Pontianak 23 5%Bali 20 5%`

Age (year) < 25 100 24%25 – 34 214 52%35 – 44 68 17%> 55 11 3%45 - 55 17 4%

Gender Male 193 53%Female 217 47%

Occupation Entrepreneur 58 34%Private Employee

126 31%

Gov. Emp. 67 16%House Wife 18 5%Others 141 34%

Education Level

High School 115 28%Diploma 43 11%S1 182 44%S2 52 13%S3 18 4%

Monthly Income (Rupiah)

<3 mio – 4 mio

163 40%

5 mio – 7 mio

96 23%

8 mio – 10 mio

70 17%

> 10 mio 81 20%Marital Status

Single 283 69%Married 127 31%

Source of Learning E-Commerce

Learning by Doing

314 56%

Friends/Other people

166 30%

TV 45 8%Book 33 6%

Monthly Expenses

Food 261 37%Clothing 157 22%Transportation

177 25%

Education 85 12%Others 29 4%

Table 2.2Measurement Sources

Construct Measurement SourcesPerceived Usefulness Wijaya (2005)Perceived Ease of Use Davis (1989), adopted by

Yoon (2009), Barket et. al. (2003), F.C Tung et. al. (2008)

Power Distance Hoftstede (2010)

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Individualism Hoftstede (2010)Masculinity Adopted by Yoon (2009)Uncertainty Avoidance Adopted by Yoon (2009)Long-Term Orientation Hoftstede (2010),

adopted by Yoon (2009)Indulgence Adopted by Yoon (2009)Intention to Use Hoftstede (2010)Actual Use Davis (1989) and

adopted by journal Carlota-Rolenzo, et al (2014)

3.2 Measurements

The questionnaire used for data collection contained scales to measure the various constructs of the model. Table 2 is the measurement that used in this researcher, based on the indicator and adoption. A pilot test of the measures was conducted by Indonesian. Individuals indicated their agreement or disagreement with the survey items using a four-point Likert-scale. The wording of items was modified on the basis of results of the pilot test. Appendix A shows all items used in the research.

4. Results

SEM was used to validate the research model, while PLS-Graph Version 2.0 was used to perform the analysis. Although the LISREL technique is widely used for the analysis of empirically based studies, PLS is more appropriate in analyzing the moderating effects of this research.

4.1 Outer Model (Measurement Loading)

PLS was used to test the convergent validity, discriminant validity and composite reliability of the scales. In a convergent validity by PLS, convergent validity is proved if a measurement loads highly - with a coefficient above 0.60 or very significantly (if its t-values are within the 0.01 level of their assigned construct) (Gefen, 2005). Table 4.1 shows the factor loadings of the measurement items and t-values. The factor loadings therefore demonstrate convergent validity, and all t-values are also above 1.96. Discriminant validity is shown when:

1. Measurement items load more strongly on their assigned construct rather than on the other constructs in the Convergent Validity.

2. The square root of the Average Variance Extracted (AVE) of each construct is larger than its correlations with the other constructs.

As shown in Table 4.1, all the measurement items loaded considerably more strongly on their respective factor than on the other constructs. Table 4.2 shows the square root of AVE and the inter-construct correlations. Comparison of the correlation with the squared AVE shows that all correlations between two constructs were less than the squared AVE of both constructs.

Table 4.1Results of Outer Loading Convergent Validity

Original Sample (O)

Sample Mean (M)

Standard Deviation (STDEV)

Standard Error (STERR) T Statistics (|O/STERR|)

AU.1 <- Y 0.848 0.848 0.018 0.018 48.275AU.2 <- Y 0.885 0.885 0.012 0.012 72.466

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AU.3 <- Y 0.886 0.885 0.014 0.014 65.568AU.4 <- Y 0.891 0.891 0.012 0.012 73.868IDV.1 <- X4 0.934 0.933 0.007 0.007 139.142IDV.2 <- X4 0.944 0.944 0.006 0.006 153.241IDV.3 <- X4 0.920 0.919 0.007 0.007 123.453IDV.4 <- X4 0.956 0.956 0.004 0.004 226.348IND.1 <- X8 0.924 0.924 0.011 0.011 84.047IND.2 <- X8 0.835 0.833 0.026 0.026 31.636IND.3 <- X8 0.807 0.807 0.033 0.033 24.746IND.4 <- X8 0.852 0.849 0.023 0.023 37.571ITO.1 <- Z 0.774 0.774 0.025 0.025 30.356ITO.2 <- Z 0.782 0.781 0.027 0.027 28.829ITO.3 <- Z 0.885 0.885 0.012 0.012 75.097ITO.4 <- Z 0.797 0.797 0.022 0.022 35.827LTO.1 <- X7 0.916 0.916 0.009 0.009 103.398LTO.2 <- X7 0.899 0.899 0.012 0.012 74.199LTO.3 <- X7 0.893 0.893 0.011 0.011 83.507LTO.4 <- X7 0.469 0.466 0.063 0.063 7.485MAS.1 <- X5 0.940 0.941 0.008 0.008 124.549MAS.2 <- X5 0.937 0.938 0.007 0.007 131.366MAS.3 <- X5 0.926 0.925 0.009 0.009 103.531MAS.4 <- X5 0.913 0.912 0.012 0.012 77.593PD.1 <- X3 0.933 0.932 0.007 0.007 131.983PD.2 <- X3 0.942 0.943 0.008 0.008 123.441PD.3 <- X3 0.933 0.933 0.008 0.008 110.857PD.4 <- X3 0.913 0.913 0.010 0.010 92.918PEOU.1 <- X2 0.882 0.881 0.011 0.011 77.699PEOU.2 <- X2 0.866 0.865 0.014 0.014 60.377PEOU.3 <- X2 0.789 0.789 0.024 0.024 33.435PEOU.4 <- X2 0.743 0.740 0.025 0.025 29.394PU.1 <- X1 0.808 0.807 0.023 0.023 35.560PU.2 <- X1 0.891 0.891 0.010 0.010 84.892PU.3 <- X1 0.894 0.893 0.012 0.012 72.081PU.4 <- X1 0.901 0.900 0.010 0.010 90.598UA.1 <- X6 0.907 0.907 0.009 0.009 104.518UA.2 <- X6 0.921 0.921 0.007 0.007 137.325UA.3 <- X6 0.887 0.887 0.012 0.012 76.646UA.4 <- X6 0.479 0.472 0.085 0.085 5.639

Table 4.2Average Variance Extracted (AVE)

Construct Variable

Average Variance

Extracted (AVE)X1 0.765X2 0.676X3 0.866X4 0.881X5 0.863X6 0.833X7 0.821X8 0.732Y 0.771Z 0.657

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Based on Table 4.1 Outer Loading Convergent Validity, the results shows that, in indicator 4th of Long-Term Orientation and 4th indicator of Uncertainty Avoidance should be omitted since the outer is smaller than 0.5. As for AVE, the output of AVE value for each construct is greater than 0.5, so it can be concluded that perceived usefulness (X1), perceived ease of use (X2), power distance (X3), individualism (X4), masculinity (X5), uncertainty avoidance (X6), long-term orientation (X7), and indulgence (X8) influencing intention to use (Z), as well as actual usage (Y) influence are

good models, so all constructs in the model are estimated to require the criteria of discriminant validity.

The third part of the outer model is to test composite reliability. Composite reliability tests the reliability value between the indicator blocks of the constructs that created it. The construct is considered reliable if the value of composite reliability and cronbach alpha is above 0.7. Composite reliability considered as good if the value is above 0.70. The value for all of the variables is more than 0.7. It represents in Table 4.3.

Table 4.3Composite Reliability

Construct Variable Composite ReliabilityX1 0.928X2 0.892X3 0.963X4 0.967X5 0.962X6 0.938X7 0.932X8 0.916Y 0.931Z 0.884

4.2 Inner Model

The model evaluation uses R-Square (R2) for the dependent construct. The R-Square value reflects the predictive power of the whole model (Pirouz, 2006) with the R-

Square value greater than 0.10 or greater than 10 percent (or the goodness-fit of the model). Based on data processing with PLS, the coefficient of determination (R-Square) is presenting in Table 4.4.

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Table 4.4R-squared

Construct R-SquareX1X2X3X4X5X6X7X8Y 0.202Z 0.632

Based on the table 4.7 shows that the R-square for the variable influence actual usage (Y) is 0.202, which means the influence actual usage (Y) influenced by influence Intention to Use (Z) in the amount of 20.2%, while other 79.8% influenced by other factors. The value of R-square for intention to use variable (Z) is 0.632, it means that the Intention to Use (Z) is influenced by perceived usefulness (X1), perceived ease of use (X2), power distance (X3), individualism (X4), masculinity (X5), uncertainty avoidance (X6), long-term orientation (X7), indulgence (X8) in the amount of 63.2%, while the other 36.8% is influenced by other factors. As for the variable perceived usefulness (X1), perceived ease of use (X2), power distance (X3), individualism (X4), masculinity (X5), uncertainty avoidance (X6), long-term orientation (X7), indulgence (X8) does not have R-square, because those factors are independent variables that influence the dependent variable, in this case is influencing intention to use (Z).

Goodness of fit in PLS can be known from the value of Q2. The value of Q2 has the same meaning as the coefficient of determination (R-Square/R2) in the regression analysis. The higher the R2, the model can be concluded to be more fit with the data. The Q-Square value greater than 0 (zero) indicates that the model has predictive relevance, while the Q-Square value less than 0 (zero) indicates that the model lacks predictive relevance (Imam Gozali, 2006).

Q2 = 1 – (1 – R21 ) (1-R22) (1 – R23) …. (1 – R 2n) = 1- (1-0.202) (1-0.632) = 1- 0.2934 = 0.7066

In this research, the R-Square value generated in the equation of the overall model is 70.66%, which is quite high, hence it means that the structural model has high predictive relevance, the better and feasible model to be used in the prediction.

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4.3 Hypothesis Test

Hypothesis testing and relationships between variables can be seen from the results of path coefficient on the model. Following is the result of path coefficient from inner model (Table 4.5).

Table 4.5Path Coefficient

Original Sample

(O)

Sample Mean (M)

Standard Deviation (STDEV)

Standard Error

(STERR)T Statistics (|O/STERR|)

Significant

X1 -> Z 0.165 0.162 0.043 0.043 3.849 Yes

X2 -> Z 0.278 0.284 0.042 0.042 6.635 Yes

X3 -> Z -0.211 -0.209 0.042 0.042 5.005 Yes

X4 -> Z -0.016 -0.017 0.036 0.036 0.445 No

X5 -> Z -0.175 -0.177 0.042 0.042 4.134 Yes

X6 -> Z 0.195 0.191 0.035 0.035 5.532 Yes

X7 -> Z 0.071 0.074 0.038 0.038 1.855 No

X8 -> Z 0.036 0.035 0.034 0.034 1.052 No

Z -> Y 0.450 0.451 0.043 0.043 10.471 Yes

5. Discussions and Conclusions

The index is t-statistic score should be more than 1.96 with error α=5%. Based on Table 4.5, perceived usefulness, perceived ease of use, power distance, and masculinity are stated as hypothesis accept. Individualism, uncertainty avoidance, long-term orientation, indulgence are hypothesis rejects. Similar finding coming from Yoon (2009) that it is unexpected. However, This lack of influence may be caused by an explanation offered by Srite and Karahanna (2006), who suggested that cultural values may play different roles depending on the stages of the acceptance process, especially at the initial acceptance or adoption decision.

The recommendation for future research is to consider some aspects such as:

1. Since this research is still not explain the dependent variable, next researcher can consider some variables to be added such as trust, attitude factors, etc. from behavior technology acceptance.

2. The model itself can be improved on cultural dimensions become the moderate or mediating.

3. As for the area of sampling, researcher can select some other countries such as Malaysia, Singapore, Thailand, China, or other Asian Country to compare with this research.

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4. After populate the questionnaire, it can be good if researcher can perform interview to respondents. Hence researcher can consider the reason of respondents select that answer.

5. Analyze in one e-commerce company also can be more specific to be research.

Appendix A

Likert scale strongly disagree (1) – strongly agree (4)

Perceived Usefulness (X1)By e-commerce, I can find the product information.By e-commerce, I can buy the product and perform transactions.

By e-commerce, I can save time and energy from find and buy the product.

By e-commerce, I can find my needs.

Perceived Ease of Use (X2)By high speed internet connection, I can easily access the e-commerce wherever whenever.I can understand e-commerce quickly without wasting my time.

I am using my basic skills to operate e-commerce.I can easily find the product through e-commerce.

Power Distance (X3)The person in a higher position should make a decision.

The person in a higher position should be respected by people who are in a lower position.The person in a lower position can do the task of person in a higher position.

The person in a higher position should give clear explanation if would like to give the task to person in a lower position.Individualism (X4)I have strong loyalty to the group(s) that I belong to.Harmony in the group(s) should be maintained.My point of view to the person is based on what group(s) they belong to.

The decision should be decided together.

Masculinity (X5)A man should be tender and a woman should be strong.A job with high earnings is better than a job with high quality of life.

A man should be ambitious and a woman should be assertive.The fulfilment of tasks is more important than caring for others.

Uncertainty Avoidance (X6)When starting a new job, I am afraid doing it.I am afraid of uncertainty about the future.

I am afraid of ambiguous situations and unfamiliar adventures.

Long-Term Orientation (X7)I am thrift.I am persistence.I like to invest my wealth

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Indulgence (X8)I am happyI like to do something that I love to do.

Leisure is important.

Freedom of speech is a primary concern.

Intention to Use (Z)I intent to use e-commerce as a media to buy a product.I intent to find my needs in e-commerce now and the future.I intent to use e-commerce more frequently.

I intent to recommend e-commerce to other people.

Actual Usage of E-Commerce (Y)Instruction:Strongly Disagree (1 – 2 times per month) Disagree (3 – 6 times per month)Agree (6 – 8 times per month)Strongly Agree (>8 times per month)

Question:Frequent to use e-commerce as media to find the product per month.

Frequent to use e-commerce as media to buy the product per month.

Instruction:Strongly Disagree (1 – 15 minutes)Disagree (15 – 30 minutes)Agree (30 – 60 minutes)Strongly Agree (>60 minutes)

Question:Frequent to use e-commerce as media to find the product per day

Frequent to use e-commerce as media to buy the product per day

REFERENCES

Gefen D. 2000. E-commerce: The Role of Familiarity and Trust Omega. The International Journal of Management Science 28: 725-737.

Ghozali, Imam. 2008. Structural Equation Modeling Metode Alternatif dengan Partial Least Square, edisi kedua. Semarang : UNDIP.

Hofstede, G., Hofstede, G. J. & Minkov, M. 2010. Cultures and Organizations: Software of the Mind (Rev. 3rd ed.). New York: McGraw-Hill.

Hofstede, G. 2001. Culture's Consequences: Comparing Values, Behaviors, Institutions and Organizations across Nations. 2nd Edition. Thousand Oaks CA: Sage Publications.

Ministry of Communication and Information Technology. 2012. White Book of Indonesia Communication and Information. Periodic Booklet. Jakarta: Indonesia IT Policy Research Group.

Davis, F.D. 1989. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, MIS Quarterly 13 (3): 320.

Pavlou, P.A. 2003. Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance

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Model. International Journal of Electronic Commerce 7(3): 101-134.

Saroja, S. 2012. Information technology –Key success factor in retail. Gian Jyoti E-Journal (2)1 221-233.

Srite, M. & Karahanna, E. 2006. The role of espoused national cultural values in technology acceptance. MIS Quarterly 30(3): 679-704.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. 2003. User acceptance of information technology: Towards a unified view. MIS Quarterly, 27(3): 425-478.

Venkatesh, V., Davis, F. D. 2000. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science 46(2): 186–204.

Yoon, C. 2009. The effects of national culture values on consumer acceptance of e-commerce: online shoppers in China. Information & Management 46(5): 294-301.

Zakour, A. B. 2004. Cultural differences and information technology acceptance. In Proceedings of the 7th Annual Conference of the Southern Association for Information Systems, Savannah, Georgia 156-161.

Zakour P. 2009. Good Manufacturing

Practices. Mexico: Wiley.

.

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