THE FACTORS THAT INFLUENCING THE JOB PERFORMANCE OF DRIVERS WHO WORKS IN THE 4.0 ONLINE TRANSPORTATION INDUSTRY

The purpose of this research is to test the effect of self-efficacy, training, wages and reward satisfaction to the driver’s job performance working in Indonesia’s online transportation. Measurement tool used in this research was the questionnaire given to 150 respondents who are working in Indonesia’s online transportation company. The data analysis used is the SEM (Structural Equation Modeling). The result shows that selfefficacy, training, wages and reward satisfaction has positive impact on job performance. This finding provides practical implications to companies in the 4.0 industry especially online transportation in Indonesia, to enhance the quality of training, self-efficacy, wages and reward satisfaction so that the driver’s performance becoming better and has positive impact to the company.


INTRODUCTION
The tense competition and the growth of industry 4.0 have shift many companies to use sophisticate technology for their industry (Lin, et al. 2017).With this growing competition, every company is forced to utilize and to maximize its potentials in an effort to stay competitive in the uneven market and hyper-competitive condition (Ghobakhloo, 2018). The technological advances have impacted the development of the social economy in many countries. Based on this condition the big development in the economy has created innovation in future competition such as the 4.0 Industry in Germany and Internet Industry in the USA (Chai, et al. 2017).
The fast development of technological advances, especially in the industrial field has pushed all parties in many fields to become more developed and using smart technology. Chai, et al. (2017) stated that the future web-based industrial operational system brings more positive impacts to the company compared to the traditional industrial operational system. In the research by Müller (2019) states that the 4.0 industry is a digital-based industry to give flexibility and customized both product and service. Rojko (2017) also states that the 4.0 industries could reduce production and logistics costs. The 4.0 industries are developing in Indonesia, one of those is the startup company in online transportation such as Gojek. The online transportation start-up has become the pioneer service provider that leads the 4.0 revolutionary industrial transformation in Indonesia.
The development of start-up companies especially in the field of the online transportation industry has become the background of this research. One factor supporting online transportation development is the professional driver-partners, driver holds the important rule in the online transportation industry. Driver's good performance and professional human resources have a positive impact on company performance and can enhance the company's competitive advantage (Hsu, Lai, & Lin, 2014).
The main question of the matters in this research is what are the factors that could support and affecting the driver's performance in the online transportation industry in Indonesia. There are a lot of developing researches about the factors that affecting the employee'sperformance in a company, GAP on this research would like to test whether those factors also impacting the online drivers in Indonesia's online transportation industry.
The first factor researched by online transportation companies toenhance the driver's performance is training. Training is an important tool for organization to repair the personnel's performance and the success of the organization. Companies conduct training in effort to enhance the employee's performance (Elnaga & Imran, 2013). The second factor, after the training being practiced to enhance the employee's performance and the driver started to work in the field, is whether the self-efficacy exist inside the driver, where the driver has confidence on their ability to succeed a task (Ballout, 2009). The theory said that driver's high self-efficacy could support their job performance. The third factor which is always the main concern among the drivers is the wages remuneration, satisfaction reward. Umar (2014) states that wages is a compensation given to the employee as reward of their contribution to the company, and reward/ acknowledgement is monetary result and real reward accepted by employee as a part of working relation, and the satisfaction of reward awarding would support the employee's job performance (Sihombing, et al 2016).
This research focusing on three determinant aspect that impacting the job performance, whether the training, self-efficacy, wages and reward satisfaction felt by the online transportation's driver in Indonesia has impact on their job performance.

Theory of Planned Behavior
The Theory of Planned Behavior which was invented by Icek Ajzen, is a theory that connects the confidence and behavior of a person. This theory is used in this research to formulate hypotheses. The Theory of Planned Behavior is the extension of The Theory of Reasoned Action which states that a person's intention toward behavior was formed by two main factors which are 'attitude toward the behavior and subjective norms, while Theory of planned behavior adds one more factor which is 'the perceived behavioral control' (Ajzen, 1991). Attitude is something in a person that can provide a positive or negative response to the existing assessment. Subjective Norm is a person's perception of the thoughts of others that can support or hinder their attitude in doing something, because someone does something because they are motivated by others. Perceived Behavioral Control is a person's perception of difficulties or ease in behavior.
The Planned Behavior Theory explains that the attitude toward behavior is an important matter that able to predict an action, though it is necessary to consider a person's attitude in testing the subjective norms and measuring the perception behavior control of that person. When the are some positive attitudes, support from others, and easiness perceive as no such obstacles to behave present, then a person's intention to behave would be higher (Ajzen, 1991).

Performance Theory
The performance theory is sourced from many fields, but the most relevant to this research are the works of Turner (1987) and Schechner (1985) Individual performance is very important for the organization and the related individual to work at Performance consist of behavior and result, as they are multi dimensional (Sonnentag & Frase, 2001).

Hypotesis Development Training and Job Peformance
According to Polo, Cervai, & Kantola (2018) training is an activity to promote an employee to do competency study on knowledge, skill, and manner that would be useful for work. Training enables an employee to do a current job in a more efficient manner and to prepare for a higher job level (Amin, Saeed, & Lodhi, 2013).While according to Dessler (2015) training is a process of study about the required basic skill by both the old and new employees to do their job.
The research conducted by Ibrahim, Boerhannoedding, & Bakare (2016) explains that there is a positive impact between methodology training and soft skill to employee performance. The training methodology (time-framed study) is expected to impact the employee's soft skill transfer acquired during the training to enhance their job performance. The quality of the company's conducted training has impacts to enhance the employee's skill, so then the performance increased (Dimri & Misra, 2008). The research by Elnaga & Imran (2013) also states that training has an impact on employee's performance. The training program is a stimulant that the employee requires to enhance their performance and skill so then the organization's productivity increased. Because of that, the training must be designed based on the specific company's necessity and goals. Effective training is a wise intervention designed based on study achievement required to the increased employee's performance.
Well-known online transportation company in Indonesia always conducting training for their drivers, were those training aligned with the literature review and the research before which suggest that training on drivers could increase the job performance? Respondents on this research has been ensured that they were trained before, the researcher makes sure on this matter by questioning before handing the questionnaire. H1 : Training has positive impacts on the driver's job performance

Self-Efficacy Training and Job Peformance
Self-efficacy according to Kreitner & Kinicki (2003) is someone's self-confidence on the opportunity in accomplishing certain tasks. A similar thing also stated by Bandura (1997) that self-efficacy is stated as an individual's confidence or their inability to show certain behavior. Self-efficacy can be stated as a personal factor that separating everyone's transformation.
Previous research, Ballout (2009) conclude self-efficacy has strong affluence and significant on career's success. In fact, Self-Efficacy acts as a self-motivation mechanism, where is someone sees high competence in him then they will set their own goals and automatically producing more efforts and persistence to accomplish that Guglielmi, et al. (2012). Mckeown & Cochrane (2017) also shows that self-efficacy is a variable that has a significant impact on work engagement. This second hypothesis is to test whether the driver's self-efficacy who works in the online transportation industry is giving positive impacts to job performance. H2 : Self-efficacy has a positive impact on the driver's job performance

Wages and Reward SatisfactionTraining and Job Peformance
According to Umar (2014) wages is compensation given to the employee as a reward for his contribution to the company. From the previous research Umar(2014) concluded that the higher the wages acquired by the employee, the higher the motivation to do the work. While according to Sihombing, et al (2016) acknowledgment or reward is a remuneration form and real wages acquired by the employee as a part of a working relationship. A good reward system is those that focus on rewarding the employee, this could stimulate the employee to produce better performance Njanja, et al. (2013).
Reward has proven to give work satisfaction to the employee and a good incentive system that could motivate the employee to enhance both productivity and company's profit in Asia (Taba, 2016;Yang & Chen, 2018;Tse, Zhang, & Jia, 2018;Thomas, 2004), reward also proven to give positive impact to organization (Kokubun, 2016). In the Online transportation industry, it is often becoming the main issue that the big wages and reward satisfaction were the triggers for the driver to reach the targeted revenue. The third hypothesis in this research would like to test whether it is aligned with the previous research which states that wages and reward satisfaction has a significant influence on the driver's job performance.

Research Model
The conceptual framework of hypothesis development can be seen in figure 1

Sample and Data Collection
The samples of this research are 150 respondent drivers who work in an online transportation company in Indonesia. The researcher acquires random samples by making sure that each driver had passed the training period given by the online transportation company. The measuring tool used on this research is the validity and reliability tested questionnaire. The questionnaire instrument on this research is training questionnaire with 9 questions developed from the previous training based research Davids, et al. (2014) and Latif (2012). The questionnaire on wages and reward satisfaction was using 11 questions developed from the previous wages and reward satisfaction based research Castro, Ferreira, & Gomes (2016) and the questionnaire on self-efficacy was using 8 questions developed from the self-efficacy research Cetin &Askun, (2018) andBallout, (2009). For the job performance questionnaire, the researcher uses 10 developed questions based on the research conducted by Koopmans, et al. (2013) developed research to measure individuals. The questionnaire data collecting period was three months from July to September 2019.

Data Analysis
Aligned as the research purpose that to test the impact/ influence of self-efficacy, training, wages and reward satisfaction to job performance, then the Structural Equation Modeling (SEM) were used. Structural Equation Modeling has two models, those are measuring model and structural model.The data analysis also presenting the descriptive analysis to sharpen the study.

Respondent Characteristics
The respondents of this study were online drivers transportation company in the surrounding Bandung area and male sex. he respondents were 20 to 35 years old also had the lowest junior high school education and the highest diploma level education

Descriptive Analysis
Data descriptive analysis can be used to enrich the study, through the data illustration it will be known how is the researched variable's condition. According to Cooper & Schindler (2014:401) descriptive analysis can be done through both central tendency and variability measure. The central tendencies are: mean, median and modus, while variability measures are range of scores and deviation standard. This research uses mean value and deviation standard to illustrate the condition of each variable. The answer score's mean value and deviation standard are useful to illustrate the general state of selfefficacy, training, wages, reward satisfaction, and job performance on 4.0 industries. Source: Processed Data, SEM 2019 Self-efficacy (SE) was measured by using 8 indicators and based on the respondent's answers result, it was acquired the average score of 4,03 and closer to scoring 4 on the scale of 1-5. This means that most of the drivers who work in the 4.0 industries already have strong self-efficacy. The number of respondents above the average score was more than those respondents with scores that below the average. Training (TR) was measured by using 9 indicators and based on the respondent's answers result, it was acquired the average score of 4,21 and closer to score 4 on the scale of 1-5. It means that training on most of the 4.0 online transportation industries was good already. The number of respondents above the average score was more than those respondents with scores that below the average.
Wages and Reward Satisfaction (RW) were measured by using 11 indicators and based on the respondent's answers result, it was acquired the average score of 3,71 and closer to score 4 on the scale of 1-5. This means that the wages and reward satisfaction on most drivers who works in the 4.0 industries were already effective. The number of respondents above the average score was more than those respondents with scores that below the average. Job performance (JP) was measured using 10 indicators, and based on the respondent's answers result, it has acquired an average score of 3,82 and closer to score 4 on the scale of 1-5. This means that the driver's performance who works in the online transportation industries were good already. The number of respondents above the average score was more than those respondents with scores that below the average.

Structural Equation Modeling
Aligned as the research purpose that to test the impact/ influence of self-efficacy, training, wages and reward satisfaction to job performance, then the Structural Equation Modeling (SEM) were used. Structural Equation Modeling has two models, those are measuring model and structural model.

Model's Goodness of Fit Test
The model's goodness of fit test was performed to find out whether the acquired model was correct/ fit to ilustrate the relation between the studied so that it can be categorized as a good model (Hair et. al, 2014:576). The model's goodness of fit test in structural equation modeling (SEM) can be seen based on several goodness of fit test criterias as presented below: The result of goodness of fit test using  2 (chi-square) test, acquires the value of 1244,1with p-value 0,000. According to Hair et al, (2014;577) in sturctural equation modeling, it was not desired the p-value smaller than 0,05. Back to the result above , it can be seen that p-value is smaller than 0,05 showing that  2 test were significant, based on the 2 test result, then the acquaired model has not overall fullfilling the good model critirea, but still according to Hair et al, (2014;578), the difficulties of acquiring p-value bigger than 0,05 onchi square test, then some goodness of fit tests were developed.
The other kind of measurement that still have connection with  2 test is Root Mean Square Error of Approximation (RMSEA).Some good values of RMSEAare still debated, but according to Hair et al, (2014;579) if the RMSEA wereunder 0,08, then the model can be accepted.On table 2 it can be seen that the RMSEA value of 0,077 is still smaller than 0,08 so that if referring to the RMSEA value, the model has fulfill the good model criteria. Also if it is seen from the value of Normed Fit Index (NFI), Incremental Fit Index (IFI)and Comparative Fit Index (NFI), they all shownas bigger than 0,9and fulfill the good model criteria.The result of goodness of fit shows that the acquired model has fullfill goodness of fit criteria on RMSEA measuringand RMR (< 0,08), and thenNFI, NNFI, IFI, RFIand CFI ( > 0,90) so that it can be concluded that the model estimation result can be accepted, which means that the acquired empirical model were aligned/ fit the theoretical model.

Measuring Model's Evaluation
Measuring model is a model that connects the latent variable to the manifest variable. Through the measuing model it will be known which indicator that was more dominant in refelcting the latent variable Hair et al, (2014;605) stated that when the manifest variable factor loading value smaller than 0,50 then the related manifest variable is suggested to be taken out from the model. This research has 4 latent variables with 38 manifest variables. Self-efficacy latent variables consist of 8 manifest variables, Training latent variable consist of 9 manifest variables,wages dan reward satisfactionconsist of11manifest variables and job performanceconsist of 10 manifest variables.
The goodness of fit test conclude that the model can be accepted, which means that the acquired model can be used to test the proposed research hypothesis. By applying therobust maximum likelihood estimation method, the full model diagram on the impact/ influence of self-efficacy, training and reward to the job performance as shown in figure 2:

Figure 2. Standardized Coefficients Full Model
Source : Processed Data, SEM 2019 As can be seen in figure 2, all the coefficients tracks has potitive value. To find out whether the used indicators to measure the latent variables have the high level goodness of fit, then the construct reliabilityandvariance extracted calculation were performed.
Here are the results of construct reliability and variance extracted calculation for each latent variable. According to Hair et al, (2014;605) the considered satisfying construct reliability (CR) is bigger than 0,70 and average variance extracted (AVE) hoped to be bigger than 0,50. On self efficacy latent variable, the variance extracted value of 0,547 shows that averagely 54,7% of information existed on each dimentions can be represented throughself efficacy latent variable. The self efficacy latent variable's construct reliability value of (0,906) is still bigger than the recommended 0,70.The training latent variable's variance extracted value of 0,567 shows that averagely 56,7% of informations exsisted on each indicator can represented through training latent varible. The training latent variable construct reliability value of (0,921)is still bigger than the recommended value of 0,70.
The wages and reward satisfaction latent variable's variance extracted value of 0,576shows that averagely 57,6% of informations existed on each dimention can be represented through the reward latent variable. The reward latent variable's construct reliability value of (0,937) is still bigger than the recommended value of 0,70. Lastly the job performance latent variable's variance extracted value of 0,576 shows that averagely 56,7% of informations exsisted on each indicator can represented through job performance latent variable. The job performance latent variable's construct reliabilityvalue of (0,931) is still bigger than the recommended value of 0,70.

The Structural Model Evaluation
After the measuring model of each latent variable are analized, then it will be explained the structural model that will the exogenous latent variable to the endogenous latent variable.Based on the result of data processing, then acquired the structural equation as follows: JP = 0.356*SE + 0.175*TR + 0.358*RW, Error var.= 0.421 , R² = 0.579 (0.0948) (0.0864) (0.0773) (0.0605) 3.755 2.027 4.633 6.961 Through the R-square value it is acknowledged that self efficacy, training, wages and reward satisfactiongiving impact as big as 57,9% to thejob performance. While the rest 42,1% are another factors aside ofself efficacy, training, wages andreward satisfaction.

The Impact of Training to the Job Performance
Based on the data of the above equation, it is seen that tstatistical value oftraining variable tojob performance (2,027) and bigger than tcritical (1,96). Because of tstatistical value is bigger compared to tcritical, so then at the error level of 5% it is decided to deny Ho1 so that Ha1 accepted. So, based on the test result, it is concluded that training has positive impact to job performanceof 4.0 industry. The research result provides empirical evidence that the better the work training, the better the job performance. The training aims to make employees able to do their jobs more efficiently and be able to prepare their jobs better so that with training employees can improve their performance. Based on these results, this study proves the same results as the study Polo, Cervai, & Kantola (2018), Amin, Saeed, & Lodhi (2013), Dassler (2015), Dimri & Misra (2008) and Elnaga & Imran, (2013).

The Impact of Self-Efficacy to Job Performance
Based on the above equation, it is seen that tstatistical value of variable self-efficacy variable to job performance (3,755) and bigger than tcritical (1,96). Because of tstatistical value is bigger than tcritical, then on error level of 5% it is decided to deny Ho2 so that Ha2 is accepted. So, based on the test result it is can be concluded that self-efficacy has positive impact to the driver's job performance who works in online transportation company in Indonesia. This research result provide stronger empirical evidence which shows that the stronger a driver's self-efficacy, the better the job performance. Self-Efficacy is selfmotivation which perceives itself to have high competence so that it will strive to achieve what is desired. This study provides evidence similar to the research conducted by Ballout (2009), Guglielmi, et al (2012), Mckeown & Cochrane (2017).

The Impact of Wages and Reward Satisfaction to Job Performance
Based on the data at the above equation it is seen that tstatisticalvalue wages and reward satisfaction variable to job performance (4,633) and bigger than tctitical (1,96). Because tstatistical value is bigger than tcritical, then in error factor of 5% decided to deny Ho3 so that Ha3 is accepted. So based on the test result, it can be concluded that Wagesand Reward satisfaction has impact to the driver's job performance.The higher the wages acquired by the employee, the higher motivation to do the work. This study provides evidence similar to the research conducted by Umar (2014), Sihombing, et al. (2016), Njanja, et al. (2013. Taba (2016); Yang & Chen (2018); Tse, Zhang, & Jia (2018); Thomas (2004), and Kokubun (2016).

Conclusion
The research result provides empirical evidence that training, self-efficacy, wages and rewards satisfaction given by the online transportation company and enjoyed by the driver, proven to give positive impacts. the results of this study support the Theory of Planned Behavioral well as some of the studies mentioned above.
The research result on hypothesis 1 (H1) giving evidence that policy and training activity conducted by the online transportation company in Indonesia has a positive impact on the driver's performance while working in the field, the implications for the company is that training must become a scheduled program of the online transportation company.
The research result in hypothesis 2 (H2) is giving another evidence that the driver's self-efficacy has a positive impact on their performance, the online transportation company hoped to be able to accept and approve the driver application request by the high driver's self-efficacy standard. Old drivers with low self-efficacy can be trained to improve their self-efficacy.
The result in hypothesis 3 (H3) shows that wages policy applied by the company and the felt reward satisfaction by the driver give a positive impact to the related driver's performance. The company hoped to be able to consider the applied wages policy including reward satisfaction that can fulfilled employees' expectation.

Implication
The result of this research provides implications for companies especially those in the online transportation industry. Self-efficacy, training, wages and reward satisfaction could impact the driver's job performance. Also the company could implement the result by recruiting drivers with a satisfying level of self-efficacy and conduct training to enhance that for the company's partner drivers. The company better be routinely and consistently conduct training, providing relevant wages and rewards, as the result shows that those particulars could enhance the driver's job performance.

Suggestion
Based on the research's result, the suggestions for the next research is that the companies on online transportation fields are suggested to give extra attention on the selfefficacy, training, wages and reward satisfaction of each company's partner drivers. Future studies are expected to study other factors that might influence driver performance and differentiate these variables based on respondent characteristics. Future studies can also apply qualitative research methods by conducting interviews with drivers.