This research aims to: 1) analyze the factors affecting productivity, 2) analyze the most dominant factors affecting productivity, 3) determine strategies for prevention to the dominant factors. The research method includes descriptive research that aims to find out the factors affecting labor productivity in the advanced construction project of the Kaubun irrigation network in East Borneo Province. The data collection method is carried out by using questionnaire. Proportionate stratified random sampling is used to collect the research respondents from the contractors as many as 58 people. The research findings found that organizational climate factor has an effect of 79.1%, job requirements factor has an effect of 76.7%, management style factor has an effect of 69.8%, labor social factor has an effect of 69.8%, labor ability factor has an effect of 60.5%, and work environment factor has an effect of 60.5%. Moreover, the most dominant factor affecting labor productivity in the advanced construction project of the Kaubun irrigation network in East Borneo Province is the organizational climate factor that has an effect of 79.1%. The strategy for overcoming the dominant factors of labor productivity is to have 1 personnel perform a full cycle of work and create a comfortable working atmosphere and a high sense of togetherness so that personnel are not easily attracted by offers from other companies.

Анотація наукової статті з будівництва та архітектури, автор наукової роботи - Suriyanto Edi, Azis Subandiyah


Область наук:
  • Будівництво та архітектура
  • Рік видавництва: 2019
    Журнал: Russian Journal of Agricultural and Socio-Economic Sciences
    Наукова стаття на тему 'ANALYSIS ON FACTORS AFFECTING LABOR PRODUCTIVITY IN THE ADVANCED CONSTRUCTION PROJECT OF THE KAUBUN IRRIGATION NETWORK OF KUTAI TIMUR REGENCY'

    Текст наукової роботи на тему «ANALYSIS ON FACTORS AFFECTING LABOR PRODUCTIVITY IN THE ADVANCED CONSTRUCTION PROJECT OF THE KAUBUN IRRIGATION NETWORK OF KUTAI TIMUR REGENCY»

    ?DOI 10.18551 / rjoas.2019-01.37

    ANALYSIS ON FACTORS AFFECTING LABOR PRODUCTIVITY IN THE ADVANCED CONSTRUCTION PROJECT OF THE KAUBUN IRRIGATION NETWORK OF KUTAI TIMUR REGENCY

    Suriyanto Edi, Researcher Azis Subandiyah, Lecturer National Institute of Technology, Malang, East Java, Indonesia * E-mail: suriyanto Ця електронна адреса захищена від спам-ботів. Вам потрібно увімкнути JavaScript, щоб побачити її. ORCID: 0000-0002-7479-3563

    ABSTRACT

    This research aims to: 1) analyze the factors affecting productivity, 2) analyze the most dominant factors affecting productivity, 3) determine strategies for prevention to the dominant factors. The research method includes descriptive research that aims to find out the factors affecting labor productivity in the advanced construction project of the Kaubun irrigation network in East Borneo Province. The data collection method is carried out by using questionnaire. Proportionate stratified random sampling is used to collect the research respondents from the contractors as many as 58 people. The research findings found that organizational climate factor has an effect of 79.1%, job requirements factor has an effect of 76.7%, management style factor has an effect of 69.8%, labor social factor has an effect of 69.8%, labor ability factor has an effect of 60.5%, and work environment factor has an effect of 60.5%. Moreover, the most dominant factor affecting labor productivity in the advanced construction project of the Kaubun irrigation network in East Borneo Province is the organizational climate factor that has an effect of 79.1%. The strategy for overcoming the dominant factors of labor productivity is to have 1 personnel perform a full cycle of work and create a comfortable working atmosphere and a high sense of togetherness so that personnel are not easily attracted by offers from other companies.

    KEY WORDS

    Productivity, labor, project, irrigation.

    Generally, the domestic business situation is still not very encouraging. In addition, in particular, the situation and condition of the number of construction service companies that have begun to move in the field of advanced construction project of the Kaubun irrigation networks (engineering, procurement, and construction) in Indonesia is increasing. On the other hand, the company of advanced construction project of the Kaubun irrigation networks currently began to look for new formations in restructuring and focus on its business sector after the company of advanced construction project of the Kaubun irrigation networks turned upside down in the 1 997 monetary crisis. There are many companies of advanced construction project of the Kaubun irrigation networks that are out of business.

    Regarding to this matter, the competition between companies of advanced construction project of the Kaubun irrigation networks is increasingly competitive. In order for the companies of advanced construction project of the Kaubun irrigation networks to compete competitively, the company needs good human resources for the advanced construction project of the Kaubun irrigation networks; in another sense, human resources should have KSA (Knowledge, Skill, Ability / Attitude). Thus, the success of human resources in carrying out the advanced construction project of the Kaubun irrigation networks can be accurate in time, cost and quality; based on human resources of the project (Azis, 2018).

    The role of human resources (HR) will greatly determine the success or failure of organizations both corporate organizations and project organizations in achieving the vision and mission that has been set initially. So, HR has the maximum contribution in achieving organizational goals. Human resources (HR) is one of the resources contained in the organization, which includes all people who carry out organizational activities. Human

    resources are the only resources that have reason, feelings, desires, abilities, skills, knowledge, encouragement, power, and work. All potential human resources have great effect on the organization's efforts to achieve its objectives.

    Human resources are also called labor. The definition of labor according to Law No. 25/97 is every man or woman who is in and / or will do work inside and outside the work relationship with the aim of producing goods or services to meet the needs of the society. Labor is an investment if it is developed appropriately. If it is managed effectively, it will provide compensation for the company of the advanced construction project of the Kaubun irrigation networks in the form of greater labor productivity in the advanced construction project of the Kaubun irrigation networks that it runs. So, it is expected that the labor can be relied upon to achieve the objectives of the project implementation which includes the accurate cost, quality, and time. Research Problems:

    • What factors affect the low labor productivity in the advanced construction project of the Kaubun irrigation networks?

    • What dominant factors affect the low labor productivity in the advanced construction project of the Kaubun irrigation networks?

    • What are the appropriate strategies to prevent those dominant factors?

    Research Objectives:

    • Analyzing the factors affecting the low labor productivity;

    • Analyzing the dominant factors affecting the low labor productivity;

    • Determining the appropriate strategies to prevent those dominant factors.

    METHODS OF RESEARCH

    Based on the method, this research includes descriptive research that aims to determine the factors affecting labor productivity in the advanced construction project of the Kaubun irrigation networks in East Borneo Province. The method of data collection is carried out using a questionnaire. The final objectives of this research are to find out the factors affecting labor productivity in the advanced construction project of the Kaubun irrigation networks in East Borneo Province and the strategies that must be carried out to overcome them.

    This research uses a survey method by capturing respondent opinions, experiences and attitudes regarding the problems that have been experienced in the advanced construction project of the Kaubun irrigation networks in East Borneo Province, by taking primary data through questionnaires and secondary data from related institutions. Based on the factors affecting labor productivity, the factors can be determined and then followed by determining the variables to be used as questions to be measured in the form of a questionnaire.

    Population and Samples. The research population is people from contractors who know the conditions and work or are directly involved in the advanced construction project of Kaubun irrigation networks in East Borneo Province. In this research, the sample is taken randomly using disproportionate stratified random sampling (Sugiyono, 2010). From the calculation results, the total number of samples to be taken is 53 respondents. Proportionate stratified random sampling is used to take the number of respondents from contractors as many as 58 people.

    Research Variables. The research variables include the independent variables (X) which consist of: Labor Ability (X1), Labor Social (X2), Work Environment (X3), Company Management Style (X4), Job Requirements (X5), and Organizational Climate (X7 ). To obtain data through a questionnaire, the questions are arranged and linked to these variables according to their respective indicators.

    Data Collection. Data collection is carried out through a questionnaire with statement items relating to factors affecting labor productivity in the advanced project construction of Kaubun irrigation networks in East Borneo Province. In addition, the Likert scale with a range

    of 1 to 4 (strongly disagree - strongly agree) is used to find out the most dominant factors affecting the labor productivity and to avoid the middle value (doubtful) which is difficult to interpret between agreeing and disagreeing. So, the firmness of the respondents in answering questions from the questionnaire can be obtained. The items in the research variable are designed (arranged) with positive questions, so number one is a very negative response code for one of the questions, while number four is a very positive response code for one of the questions.

    Data Processing and Analysis. Data obtained from survey results (questionnaires) will later be processed to obtain information in table form. The processed data is used to answer the problem statement. Data processing should pay attention to the type of data by focusing on the objectives to be achieved. The accuracy of analytical techniques greatly affects the accuracy of the research findings. The data analysis technique used here is descriptive statistics, factor analysis and multiple linear regression analysis (Cohen et. Al., 2014 року). Data from questionnaires with a range of 1 to 4 from each variable are then reassessed. Thus, each variable containing several indicators will produce only one score value which is then analyzed using factor analysis and multiple linear regression analysis (Tabachnick & Fidell, 2007). Data processing is carried out with the help of the Statistical Package for Social Science (SPSS) program for Windows.

    Descriptive Analysis. In this research, a descriptive analysis using frequency distribution aims to explain the proportion of respondents 'answers to factors affecting the low labor productivity in the advanced project construction of Kaubun irrigation networks in East Borneo Province with a scale of 1-4 (strongly disagree - strongly agree). Descriptive analysis testing is conducted by analyzing percentages and using procedure frequencies that use SPSS tools.

    Factor Analysis. Factor analysis used in this research aims to reduce and analyze the factors that illustrate the low labor productivity. This analysis produces information about the data structure of low labor productivity. The results of factor analysis on 8 variables that are suspected as the cause of low labor productivity will go through the feasibility test of variables to determine the correlation between the variables or indicators (Everitt & Dunn, 2001). If the MSA value < 0.5 the variable will be issued then it will be recalculated until it has an MSA value > 0.5 so that the factor value is feasible to be analyzed further. Then these variables will be extracted into several main factors that have dimensions smaller than the total number of indicators (Ghozali, 2006).

    Factors that have an Eigen Value greater than one (A > 1), for example F1 and F2, it will be concluded that there are only 2 significant factors. To be able to interpret F1 and F2, the magnitude of these factors must be considered from each variable. Meanwhile, factors that have an Eigen Value smaller than one (A < 1) will be ignored. Several stages in the analysis of factors and steps in the reduction process consist of:

    • The tests used here are the KMO (Kaiser Meyer Olkin) Measure of Sampling Adequacy and Bartlett's Test. Then, this test is based on the correlation matrix. The results of the overall questionnaire testing are tested using the KMO (Kaiser Meyer Olkin) Measure of Sampling Adequacy, i.e. an index used to test the accuracy of factor analysis. Samples are accepted if the value of KMO Measure of Sampling Adequacy (MSA) > 0.5. In addition, the anti-image index ranges from 0 to 1. The index will be 1 if all elements of the matrix have a correlation value of zero which indicates that all attributes can be predicted without error. In other words, if the antiimage index value approaches one then it will increasingly show that all attributes can be predicted with smaller errors (Kaiser, 1974).

    • Kaiser Meyer Olkin Measure of Sampling Adequacy is a statistic that indicates the proportion of diversity in components that can be made the foundation in using factor analysis. A high value (close to 1.0) generally indicates that factor analysis is very useful for the data. If the value is less than 0.50, the results of factor analysis will be less useful (Kaiser, 1974).

    • Bartlett's Test of Sphericity is used to test the hypothesis whether or not the correlation matrix is ​​an identity matrix that will indicate that the components used are

    not correlated and are suitable for use in factor analysis. Low scores (less than 0.05) indicate that the results of factor analysis will be appropriate (Kaiser, 1974).

    RESULTS OF STUDY

    Descriptive Analysis. The data analysis test related to the factors affecting labor productivity in the advanced project construction of the Kaubun irrigation networks in East Borneo Province was carried out descriptively using frequency distribution. It is calculating the percentage of the average score of each item question to describe the level of achievement of a criterion compared with predetermined criteria.

    Labor Ability Factor (X1). Description on labor ability factor (X1) can be seen in Table 1 as follows:

    Table 1 - Descriptive Analysis of Labor Ability Factor (X1)

    Questions Frequency Percent Valid Percent Cumulative Percent

    Not influential 4 4.7 4.7 4.7

    Less influential 6 9.3 9.3 14.0

    Quite influential 9 16.3 16.3 30.2

    Influential 29 62.8 62.8 93.0

    Very influential 5 7.0 7.0 100.0

    Total 53 100.0 100.0

    On the labor ability factor, 4 respondents or around 4.7% stated that it is not influential, 6 respondents or about 9.3% stated that it is less influential, 9 respondents or around 16.3% stated that it is quite influential, 29 respondents or about 62.8 % stated that it is influential, and the remaining 5 respondents or around 7.0% stated that it is very influential. Furthermore, it can be concluded that the description of the labor ability factor (X1) found that 69.8% of respondents stated that it is influential, 16.3% of respondents stated that it is quite influential and the other 14.0% stated that it is less influential with the labor ability factor.

    Labor Social Factor (X2). Description on labor social factor (X2) can be seen in Table 2 as follows:

    Table 2 - Descriptive Analysis of Labor Social Factor (X2)

    Questions Frequency Percent Valid Percent Cumulative Percent

    Not influential 3 2.3 2.3 2.3

    Less influential 4 4.7 4.7 7.0

    Quite influential 12 23.3 23.3 30.2

    Influential 28 60.5 60.5 90.7

    Very influential 6 9.3 9.3 100.0

    Total 53 100.0 100.0

    On the labor social factor, 3 respondents or around 2.3% stated that it is not influential, 4 respondents or about 4.7% stated that it is less influential, 12 respondents or around 23.3% stated that it is quite influential, 28 respondents or about 60.5 % stated that it is influential, and the remaining 6 respondents or around 9.3% stated that it is very influential. Furthermore, it can be concluded that the description of the labor social factor (X2) found that 69.8% of respondents stated that it is influential, 23.3% of respondents stated that it is quite influential and the other 7.0% stated that it is less influential with the labor social factor.

    Work Environment Factor (X3). Description on work environment factor (X3) can be seen in Table 3 as follows in the table 3.

    On the work environment factor, 4 respondents or around 4.7% stated that it is not influential, 6 respondents or about 9.3% stated that it is less influential, 13 respondents or around 25.6% stated that it is quite influential, 21 respondents or about 44.2 % stated that it is influential, and the remaining 9 respondents or around 16.3% stated that it is very influential. Furthermore, it can be concluded that the description of the work environment factor (X3) found that 60.5% of respondents stated that it is influential, 25.6% of respondents stated that

    it is quite influential and the other 14.0% stated that it is less influential with the work environment factor.

    Table 3 - Descriptive Analysis of Work Environment Factor (X3)

    Questions Frequency Percent Valid Percent Cumulative Percent

    Not influential 4 4.7 4.7 4.7

    Less influential 6 9.3 9.3 14.0

    Quite influential 13 25.6 25.6 39.5

    Influential 21 44.2 44.2 83.7

    Very influential 9 16.3 16.3 100.0

    Total 53 100.0 100.0

    Management Style Factor (X4). Description on management style factor (X4) can be seen in Table 4 as follows:

    Table 4 - Descriptive Analysis of Management Style Factor (X4)

    Questions Frequency Percent Valid Percent Cumulative Percent

    Not influential 3 2.3 2.3 2.3

    Less influential 14 27.9 27.9 30.2

    Quite influential 28 60.5 60.5 90.7

    Influential 6 9.3 9.3 100.0

    Total 53 100.0 100.0

    On the management style factor, 3 respondents or around 2.3% stated that it is not influential, 14 respondents or about 27.9% stated that it is less influential, 28 respondents or around 60.5% stated that it is quite influential, and the remaining 6 respondents or around 9.3% stated that it is influential. Furthermore, it can be concluded that the description of the management style factor (X4) found that 69.8% of respondents stated that it is influential, 27.9% of respondents stated that it is less influential and the other 2.3% stated that it is not influential with the management style factor.

    Job Requirement Factor (X5). Description on job requirement factor (X5) can be seen in Table 5 as follows:

    Table 5 - Descriptive Analysis of Job Requirement Factor (X5)

    Questions Frequency Percent Valid Percent Cumulative Percent

    Not influential 6 9.3 9.3 9.3

    Less influential 8 14.0 14.0 23.3

    Quite influential 26 55.8 55.8 79.1

    Influential 11 20.9 20.9 100.0

    Total 53 100.0 100.0

    On the job requirement factor, 6 respondents or around 9.3% stated that it is not influential, 8 respondents or about 14.0% stated that it is less influential, 26 respondents or around 55.8% stated that it is quite influential, and the remaining 11 respondents or around 20.9% stated that it is influential. Furthermore, it can be concluded that the description of the job requirement factor (X5) found that 76.7% of respondents stated that it is influential, 14.0% of respondents stated that it is less influential and the other 9.3% stated that it is not influential with the job requirement factor.

    Organizational Climate Factor (X6). Description on organizational climate factor (X6) can be seen in Table 6.

    On the organizational climate factor, 3 respondents or around 2.3% stated that it is not influential, 4 respondents or about 4.7% stated that it is less influential, 8 respondents or around 14.0% stated that it is quite influential, 25 respondents or about 53.5 % stated that it is influential, and the remaining 13 respondents or around 25.6% stated that it is very influential. Furthermore, it can be concluded that the description of the organizational climate factor (X6) found that 79.1% of respondents stated that it is influential, 14.0% of respondents

    stated that it is quite influential and the other 7.0% stated that it is less influential with the organizational climate factor.

    Table 6 - Descriptive Analysis of Organizational Climate Factor (X6)

    Questions Frequency Percent Valid Percent Cumulative Percent

    Not influential 3 2.3 2.3 2.3

    Less influential 4 4.7 4.7 7.0

    Quite influential 8 14.0 14.0 20.9

    Influential 25 53.5 53.5 74.4

    Very influential 13 25.6 25.6 100.0

    Total 53 100.0 100.0

    From X1 to X6, the variables that have the most dominant factors are as follows: organizational climate factor with influential respondent answer of 79.1%, job requirement factor with influential respondent answer of 76.7%, management style factor with influential respondent answer of 69.8%, labor social factor with influential respondent answer of 69.8%, labor ability factor with influential respondent answer of 60.5%, dan work environment factor with influential respondent answer of 60.5%,

    Factor Analysis. A collection of variables is feasible to use factor analysis if it has a high level of dependence. The indication of this level of dependence is determined by the values ​​of KMO (Keizer Meyer Olkin) and MSA (Measures Sampling Adequacy) (Sharma, 1996). The following are the results of the selection of indicators (items) affecting the time and cost target of contract implementation. Selection is conducted on the MSA values. Variables with the lowest value of MSA items and less than 0.50 will be dropped (drop) then the recalculation is carried out until all items have MSA values ​​of more than 0.50. The analysis results of the 6 variables found that there is an item that had to be excluded because it had an MSA value of less than 0.50.

    After the selection of feasible items (screening of items), it obtained items that meet the requirements for analysis. Next is summarizing or extracting a set of items. The results of factor extraction were continued by interpreting the loading factor of each item. The factor will represent a number of items if the factor loading is more than 0.50. Loading factors also explain the amount of the correlation of an item with the factors. The results of loading factors are obtained from the Component Matrix. If there are many significant factors, difficulties are often found in the interpretation of factors due to overlapping of extracted factors. To overcome this, factor rotation is carried out. So, the results of factor extraction will be seen from the Rotated Component Matrix calculation. Varimax rotation method is used to obtain optimal factor loading (Abdi, 2003).

    Latent Variable of Labor Ability (X1). The latent variable of labor ability is measured by 5 items of questions which are descriptors of things that become a measure of the effect on labor productivity.

    Table 5 - Results of Factor Analysis for Latent Variable of Labor Ability

    Manifest Variables Communality Values ​​Loading Factor 1 Loading Factor 2 KMO MSA Significance of Bartlett's Statistics

    X1.1 0.592 0.512 0.574 0.620 0.627 0.000

    X1.2 0.751 0.542 0.676 0.539

    X1.3 0.522 0.721 0.045 0.720

    Eigen Value 1.784 1.468

    Total Diversity 35.678 29.368

    Cumulative Total Diversity 35.678 65.046

    Table 5 shows a summary of communality values, loading factors, KMO, MSA and significance of Bartlett's statistics for manifest variables (indicators) of latent variable of labor ability. Based on the above table, the loading value of Factor 1 and the MSA value are not all above 0.5 so it can be concluded that not all answers to questions for each indicator in the latent variable of labor ability can be used for further analysis. The KMO value of 0.620 has

    been above 0.5. This shows that the suitability of the application of the model with factor analysis for these variables is quite good. The Bartlett's test significance value of 0.000 is less than a (0.05). It shows that the correlation matrix between manifest variables (indicators) is not an identity matrix (it is likely that the matrix = 0). So, it can be concluded that the answers to questions for each indicator on the latent variable of labor ability can be used for further analysis. Based on the results of factor analysis with the main component analysis extraction method, it turns out that 2 significant Eigen values ​​can be obtained (> 1.0), with the cumulative percentage level of contribution to the research data amounting to 65.046%. The table above shows that all manifest variables that form latent variables of labor capability have loading factor values ​​above 0.5.

    Table 6 - Final Results of Factor Analysis for Latent Variable of Labor Ability

    Manifest Variables Communality Values ​​Loading Factor 1 KMO MSA Significance of Bartlett's Statistics

    X1.1 0.571 0.755 0.613

    X1.3 0.671 0.819 0.611

    X1.5 0.391 0.625 0.643 0.720 0.000

    Eigen Value 2.117

    Total Diversity 52.931 -

    Cumulative Total Diversity 52.931

    Latent Variable of Labor Social (X2). The latent variable of labor social is measured by 6 items of questions which are descriptors of things that become a measure of the effect on labor productivity.

    Table 7 - Results of Factor Analysis for Latent Variable of Labor Social

    Manifest Variables Communality Values ​​Loading Factor 1 Loading Factor 2 Loading Factor 3 KMO MSA Significance of Bartlett's Statistics

    X2.1 0.602 0.615 -0.277 -0.384 0.584 0.637 0.015

    X2.2 0.677 0.757 0.314 0.070 0.645

    X2.3 * 0.855 -0.008 -0.054 0.923 0.458

    X2.4 0.590 0.761 0.101 -0.018 - 0.677 -

    Eigen Value 1.573 1.457 1.220 -

    Total Diversity 26.222 24.290 20.327

    Cumulative Total Diversity 26.222 50.512 70.839

    Table 8 - Final Results of Factor Analysis for Latent Variable of Labor Social

    Manifest Variables Communality Values ​​Loading Factor 1 KMO MSA Significance of Bartlett's Statistics

    X2.1 0.380 0.616 0.680

    X2.2 0.612 0.783 0.570

    X2.4 0.582 0.763 0.592 0.577 0.020

    Eigen Value 1.574

    Total Diversity 52.455

    Cumulative Total Diversity 52.455

    Table 7 shows a summary of communality values, loading factors, KMO, MSA and significance of Bartlett's statistics for manifest variables (indicators) of latent variable of labor social. Based on the above table, the loading value of Factor 1 and the MSA value are not all above 0.5; for example, X2.3, X2.5 and X2.6 indicators, those indicators in the latent variable of labor social can not be used for further analysis. The KMO value of 0.584 has been above 0.5. This shows that the suitability of the application of the model with factor analysis for these variables is quite good. The Bartlett's test significance value of 0.0015 is less than a (0.05). It shows that the correlation matrix between manifest variables (indicators) is not an identity matrix (it is likely that the matrix = 0). So, it can be concluded that the answers to questions for each indicator on the latent variable of labor social can be used for further analysis.

    Based on the results of factor analysis with the main component analysis extraction method, 3 Eigen values ​​turned out to be quite meaningful (> 1.0), with the cumulative

    percentage level of contributing factors to the research data amounting to 70,839%. The cumulative contribution rate indicates that the effect of labor productivity on the advanced construction project of the Kaubun irrigation network in East Borneo Province can be explained by 70.839% by indicators of the latent variable of social labor (X2), while the remaining 29.161% is an error or formed by other indicators that have not been detected in this research. The table above shows that all manifest variables that make up the latent variable of labor social have loading factor values ​​above 0.5 but not all manifest variables fit in Factor 2. The latent variable is not as much as initial prediction, i.e. 3 manifest variables consisting of social availability labor (X2.1). The final results of factor analysis can be seen in Table 8 in which the reduction is made only to X2.1, X2.2, X2.4. The values ​​of KMO and Bartlett's Significance Test were 0.592 and 0.020, respectively.

    Latent Variable of Work Environment (X3). The latent variable of work environment is measured by 3 items of questions which are descriptors of things that become a measure of the effect on labor productivity.

    Table 9 - Results of Factor Analysis for Latent Variable of Work Environment

    Manifest Variables Communality Values ​​Loading Factor 1 KMO MSA Significance of Bartlett's Statistics

    X3.1 0.264 0.514 0.760 0.825 0.000

    X3.2 0.522 0.723 0.770

    Eigen Value 2.502

    Total Diversity 50.031

    Cumulative Total Diversity 50.031

    Table 9 shows a summary of communality values, loading factors, KMO, MSA and significance of Bartlett's statistics for manifest variables (indicators) of latent variable of work environment. Based on the above table, the MSA values ​​are all above 0.5 so it can be concluded that indicators in the latent variable of work environment can be used for further analysis. The KMO value of 0.760 has been above 0.5. This shows that the suitability of the application of the model with factor analysis for these variables is quite good. The Bartlett's test significance value of 0.000 is less than a (0.05). It shows that the correlation matrix between manifest variables (indicators) is not an identity matrix (it is likely that the matrix = 0). So, it can be concluded that the answers to questions for each indicator on the latent variable of work environment can be used for further analysis. Based on the results of factor analysis with the main component analysis extraction method, it turns out that only 1 significant Eigen value can be obtained (> 1.0), with the cumulative percentage level of contribution to the research data amounting to 50.031%.

    Latent Variable of Management Style (X4). The latent variable of management style is measured by 9 items of questions which are descriptors of things that become a measure of the effect on labor productivity.

    Table 10 - Results of Factor Analysis for Latent Variable of Management Style

    Manifest Variables Communality Values ​​Loading Factor 1 Loading Factor 2 Loading Factor 3 MSA

    X4.1 * 0.322 0.390 -0.156 -0.381 0.448

    X4.2 0.620 0.775 0.122 -0.069 0.590

    X4.3 0.696 0.827 -0.051 0.092 0.773

    X4.4 0.640 0.627 0.494 0.051 0.707

    X4.5 0.605 0.736 -0.103 0.230 0.454

    Eigen Value 2.439 1.700 1.616 -

    Total Diversity 27.102 18.886 17.953

    Cumulative Total Diversity 27.102 45.988 63.942

    Table 10 shows a summary of communality values, loading factors, KMO, MSA and significance of Bartlett's statistics for manifest variables (indicators) of latent variable of management style. The above table presents the values ​​of loading factor 1 and MSA.

    Table 11 - Final Results of Factor Analysis for Latent Variable of Management Style

    Manifest Variables Communality Values ​​Loading Factor 1 KMO MSA Significance of Bartlett's Statistics

    X4.2 0.582 0.763 0.744

    X4.3 0.680 0.824 0.720

    X4.4 0.500 0.707 0.823

    X4.5 0.591 0.769 0.763 0.763 0.000

    Eigen Value 2.353

    Total Diversity 58.824

    Cumulative Total Diversity 58.824

    Latent Variable of Job Requirement (X5). The latent variable of job requirement is measured by 4 items of questions which are descriptors of things that become a measure of the effect on labor productivity.

    Table 12 - Results of Factor Analysis for Latent Variable of Job Requirement

    Manifest Variables Communality Values ​​Loading Factor 1 KMO MSA Significance of Bartlett's Statistics

    X5.1 0.600 0.775 0.599

    X5.2 0.531 0.559 0.596

    X5.3 0.525 0.502 0.596

    X5.4 0.764 0.765 0.599 0.526 0.009

    Eigen Value 1.241

    Total Diversity 60.028 -

    Cumulative Total Diversity 60.028

    Table 12 shows a summary of communality values, loading factors, KMO, MSA and significance of Bartlett's statistics for manifest variables (indicators) of latent variable of job requirement. Based on the above table, the MSA values ​​are all above 0.5 so it can be concluded that indicators in the latent variable of job requirement can be used for further analysis. The KMO value of 0.599 has been above 0.5. This shows that the suitability of the application of the model with factor analysis for these variables is quite good. The Bartlett's test significance value of 0.009 is less than a (0.05). It shows that the correlation matrix between manifest variables (indicators) is not an identity matrix (it is likely that the matrix = 0). So, it can be concluded that the answers to questions for each indicator on the latent variable of job requirement can be used for further analysis. Based on the results of factor analysis with the main component analysis extraction method, it turns out that only 1 significant Eigen value can be obtained (> 1.0), with the cumulative percentage level of contribution to the research data amounting to 60.028%.

    Table 13 - Summary of Strategies for Overcoming Dominant Factors of Labor Productivity in the Advanced Construction Project of the Kaubun Irrigation Network in East Borneo Province

    No Aspect / Factor Strategies

    1 Organizational Climate Having 1 personnel to perform a full cycle of work; Creating a comfortable working atmosphere and a high sense of togetherness so that personnel are not easily attracted by offers from other companies

    CONCLUSION

    From X1 to X6, the variables that have the most dominant factors are as follows: organizational climate factor has an effect of 79.1%, job requirements factor has an effect of 76.7%, management style factor has an effect of 69.8%, labor social factor has an effect of 69.8%, labor ability factor has an effect of 60.5%, and work environment factor has an effect of 60.5%. Moreover, the most dominant factor affecting labor productivity in the advanced construction project of the Kaubun irrigation network in East Borneo Province is the organizational climate factor that has an effect of 79.1%. The strategy for overcoming the dominant factors of labor productivity is to have 1 personnel perform a full cycle of work and create a comfortable working atmosphere and a high sense of togetherness so that personnel are not easily attracted by offers from other companies.

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    Ключові слова: Productivity / labor / project / irrigation

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