RESEARCH METHODS Population

3. RESEARCH METHODS Population

The population in this study is all the customer of freight services company in Semarang, which is limited to forwarding company not direct shipper or direct exporter. In the last 6 months, there is 90 forwarding company.

Samples

The minimum samples are75 respondents based on the following formula (Rao, 1996): N n = 1 + N (moe) 2 Where:

N = the number of population n

= number of samples taken Moe

= maximum margin of error is still in the tolerance of 5%

Sampling Method

This study uses nonprobability sampling approach, with purposive samplingas the sampling method used.

Research variable

The design of this study consisted of four variables, namely:

International Conference on Business, Economics, Socio-Culture & Tourism 2016 (ICBEST2016)

1. Independent Variables,Customer orientation, Quality of services delivery, andPrice competitiveness as the independent variable.

2. Dependent Variables which is Repeat purchase intention

3. Intervening Variabel,customer value is positioned as an intervening variable affecting an indirect relationship between the independent variables of customer orientation and quality of services delivery towards the dependent variable repeat purchase intention.

4. Moderating Variabel,relational commitment is placed as variables suspected to affect the strength of the effect on the price competitiveness towards repeat purchase intention.

Method of data collecting

Data is collected through questionnaires for selected respondents using the data scale interval of 1-10 as an alternative answer to measure respondents' attitudes.

Techniques used to measure the data interval are bipolar adjective. The left end in the form of low numbers that illustrate the negative answer, and the right end in the form of large numbers illustrate the positive answer.

Analysis Technique Reliability Test

The approach to assess the reliability of model fit is composite reliability and variance extracted for every construct. Reliability coefficients ranged between 0 to 1. The higher the reliability coefficient (closer to 1), the more reliable the model is (Yamin dan Kurniawan, 2009:8).

Validity Test

A questionnaire is considered valid if the questionnaire is able to express the meaning that will be measured by the questionnaire. Confirmatory factor analysis (CFA) was used to assess the validity of each construct which is a manifestation of the indicator. If all the loading of the latens construct showed a significant result that is the t statistic> 1.645 (one-tailed) or t-statistic> 1,960 (2-tailed), then each question is a valid indicator (Ghozali, 2008).

Data analysis method

Presented by Wold (1985) in Ghozali (2008), PLS is a powerful method of analysis, because it is not based on many assumptions. The data is also not necessary to have multivariate normal distribution andlarge sample. PLS other than to confirm the theory, as well as to explain whether there is any relationship between the latent variables so that in the framework of research-based PLS predictions are more suitable for analyzing data.

Model to be analyzed in the PLS, namely:

1. Assessing outer model or measurement models

2. Assessing inner model or structural models The acceptance or rejection of the hypothesis is done with the following provisions:

1. Seeing the outer weight value of each indicator and its significance value. The recommended weight value is above 0.50 and above its t-stasistic of 2.326

2. Seeing the inner weight value of the relationship between latent variables. Weight value of the relationship must show a positive direction with the value of t-statistic is above 2.326 and p <0.01 (one-tailed). Value of t-table determined in this study is 2.326 for significance of p <0.01 (one - tailed). Furthermore, t-table value is taken as the cutoff value for the acceptance or rejection of the hypothesis.

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Research result Validity test

Unidimensionalitas test of each construct to see convergent validity of each construct indicator. According to Chin (1998) in Ghozali (2006) an indicator is said to have good reliability if the value is greater than 0.70. Loadingfactor of0.50 - 0.60 can still be maintained for a model that is still in development.

Figure 1 Output Model Structure

Source: Output SmartPLS 2.0.M3

Figure 1 shows that the models already meet the convergent validity because all of loading factor is above

A. Reliability Test

The value of a construct is reliable if it provides reliability composite value> 0.70 (Werts et al. 1974 in Imam, 2006).The test results demonstrate the value of Composite Reliability> 0.70, that means all the constructs or variables in this study showed as a fit model, meaning that all items of questions were used to measure each construct is reliable.Communality value> 0.5 also strengthens results that constructs in this study is reliable.

B. Structural Model Testing ( Inner Model)

From the value of R-Square it can be concluded that the Value Customers variable can be explained by Customer Orientation variable and Quality Services Delivery variable by 43.05% while the remaining 56.95% is explained by other variable not included in this research model. Repeat purchase intention variable can be explained by Customer Value variable, Prices Competitiveness variable, and the Commitment variable of 61.22% of the model, and 38.78% is explained by other variables not included in this research model. According to Chin (1998) in Gozhali (2006) R-Square value of 0.67 (strong), 0.33 (moderately) and 0.19 (weak).

C. Results of Inner Model Or Relationships Construct

Table 2 below gives the results of the relationship between the constructs; it turns out there is one that is insignificant relationship between the price of the interest where t indicates a value below 1.282 to p <0.05 (one-tailed). While the relationship between the other constructs showed coefficients are significant at p <0.01 (t is greater than t table 2.326) and p <0.10 (t is greater than t table 1.282).

Another criterion to measure themodel structure is to use predictive relevant Q 2 , which indicates that all Q 2 values is above zero. The Q 2 value of price is 0.509309, Q 2 value of price*commitment is 0.507054, Q 2 value of commitment is 0.486721, Q 2 value of interest is 0.616115, Q 2 value of quality is 0.278274, Q 2 value of

International Conference on Business, Economics, Socio-Culture & Tourism 2016 (ICBEST2016)

value is 0.267356, and lastly Q 2 value of orientation is 0.126198, this indicates predictive relevance for each latent variable.

Table 2 Construct Crossvalidated Communality

Total

1-SSE/SSO

harga

harga * komitmen

Sumber: Output SmartPLS 2.0.M3