Abstract
This aim of this study was to examine the moderating role that planning has on human resource development and employee performance at Perum Bulog in the regional division of South Sumatra, Indonesia. The research methodology used the structural equation model approach and was carried out using the SmartPLS 2.0.M3 program. The management of this research employed inferential statistical data analysis measured using SmartPLS software. The hypothesis testing was carried out using the structural equation model approach based on partial least squares (PLS). PLS is a component- or variant-based structural equation model. The results showed that the variable of human resource development has an effect on the performance of the Regional Office of Regional Division of South Sumatra. The results of the human resource planning test showed an effect on the performance of the South Sumatra regional division employees.
Keywords: Human resource development employee performance, Discipline, attendance, Accident rate, Level of cooperation.
Received: 19 January 2021 / Revised: 26 February 2021 / Accepted: 2 April 2021/ Published: 28 April 2021
Contribution/ Originality
This study contributes to literature on the importance of human resource planning for efficient performance.
1. INTRODUCTION
The phenomenon of comprehensive human resource planning is a good foundation and use of initial capital for the smooth implementation of work programs and the achievement of company goals. Human resource planning cultivates human resources in accordance with the quality and quantity required in line with a company's commitment to its vision and mission. Hasibuan (2017) explains that human resource planning is planning the workforce to suit company needs and is effective and efficient in the realization of goals.
Nawawi (2015) states that human resource planning is the process of determining a strategy for obtaining, utilizing, developing, and maintaining human resources according to the current needs of the company and developing them in the future. Sule & Saefullah (2012) stated that human resource development is a continuation step of the labor supply process. Human resources within organizations is a very important factor for the effectiveness of internal activities (Fitria, Mukhtar, & Akbar, 2017; Fitria, 2018; Hasanah & Kristiawan, 2019; Kristiawan, Safitri, & Lestari, 2017). To achieve this, the organization is required to improve the quality of people being recruited who will improve their performance and contribute to improving organizational performance (Irmayani, Wardiah, & Kristiawan, 2018). Quality human resources are more important than the wealth of natural resources because the wealth of natural resources cannot guarantee the welfare and success of a nation (Asvio, Yamin, & Risnita, 2019). Human resource quality will increase the quality of a country (Rahmadoni, 2018).
Widodo & Suparno (2015) explained that employee performance is the level of achievement of results for the implementation of certain tasks. Priansa (2014) stated that performance is basically measured according to the interests of the organization, so that indicators used to measure it are adjusted to suit the interests of the organization itself (Srour, Kheir-El-Din, & Samir, 2020). Along with the development of an increasingly competitive era in knowledge and insight in any field of work, competent and qualified human resources are needed to achieve company goals. Work performance is the result that is achieved by someone according to the prevailing measure, in a certain period of time, with respect to work, behavior, and actions (Fazira & Mirani, 2019).
An employee's performance is the result of the work achieved while carrying out their duties according to their responsibilities. The work of an employee can be stated in the forms of quality and quantity or how much work can be achieved or generated. The performance of a process includes reciprocity, sustainability, partnership between employees and leaders, understanding the content of their work, responsibilities, and carrying out effective work either individually or in groups (Adepoju & Aigbavboa, 2020; Anastasiou, 2020; Astuti, Fitria, & Rohana, 2020; Maryanti., Rohana, & Kristiawan, 2020; Suratman, Arafat, & Eddy, 2020). It is the employees who carry out tasks and responsibilities, and they need to understand their roles and be able to identify if work is not being carried out effectively (Arafat, 2011).
Human resource planning at Perum Bulog, in the regional division of South Sumatra, is not yet solid and must further improve the standard of capabilities so that human resources continue to increase, especially with regard to the supply of skilled labor with adequate adaptability and who are able to deal with confusion or adverse situations. Human resource development is one of the steps in improving employee quality (Hermawati, Anam, & Suhermin, 2020). Training and human resource development is expected to have a positive impact on employees and companies in the future. Based on research conducted by Prasetiawati (2015) at the Department of Transportation, Communication and Information, Tanah Bumbu Regency, the results showed that planning on its own does not have a significant effect on performance, but planning and supervision do have a significant effect on employee performance. Meanwhile, Elaine, Said, & Wandary (2016) concluded that training and development programs on employee performance showed that from the two independent variables only the training program had a significant effect on employee performance (Maryati, Fitria, & Rohana, 2020; Suratman et al., 2020). Meanwhile, the training and development program variables simultaneously had no significant influence on employee performance variables.
Various studies have been conducted on the above phenomena to analyze the effects of human resource development and planning on employee performance. However, the results were not always consistent and this study aims to further investigate the claim that human resource planning has a positive impact in the achievement of excellent performance.
2. RESEARCH METHODS
According to Uma & Roger (2017), the research method is the line of thought of a study. The method used in this research is a quantitative method that uses statistical formulas that are adjusted to the research topic and problem formulation to analyze the data.
The data source comes from the total score obtained from respondents who filled out a questionnaire that had been sent to the employees of Perum Bulog in the regional division of South Sumatra. In this study, the population is all 80 employees of Perum Bulog. Because the population is fewer than 100, this study used a saturated sampling technique, or census sampling.
The research hypothesis testing was carried out using the structural equation model based on partial least squares, which is a structural equation model based on components or variants; the structural equation model is a field of statistical studies that can test a relationship that is relatively difficult to measure simultaneously. According to Santoso (2014), it is a multivariate analysis technique, which is a combination of factor analysis and regression/correlation analysis which focuses either on indicators and their constructs, or relationships between constructs/latent variables or theories, while PLS is more of a predictive model. However, there is a difference between covariance-based and component-based PLS in the use of structural equation models to test theories or theory development for prediction purposes.
This study used a questionnaire to collect research data. To determine the level of validity and reliability of the questionnaire, the study used the SmartPLS 2.0 program. A convergent validity testing procedure was used, which correlates the item score with the construct score, and then produces the loading factor value.
3. RESULTS AND DISCUSSION
Based on empirical testing that has been carried out on several hypotheses in the study, the results indicate that not all of the exogenous variables above have a significant effect on endogenous variables.
3.1. Human Resource Development Variable (X)
Table 1 shows the results of respondents on the human resource development variable (Z), which are used to measure the descriptive frequency; 1) the work performance indicator (X1.1) shows that the average score is 230.1 in the low category; 2) the discipline indicator (X1.2) obtained the average score with the statement that employees always arrive and leave on time (the average score given reaches 280.0 with the category of adequate answers); 3) the attendance indicator (X1.3) obtained an average score of 255.7 in the low category; 4) the accident rate indicator (X1.4) obtained an average score of 283.3 in the sufficient answer category; 5) the cooperation level indicator (X1.5) gave an average score of 216.3 in the low answer category; 6) Praaksara indicator (X1.6) with an average score of 315.0 in the sufficient answer category; 7) wage level (X1.7) obtained an average score of 336.7 in the sufficient answer category; and 8) leadership and managerial decisions (X1.8) gave an average score of 320.8 in the neither/nor answer category.
3.2. Human Resource Planning Variable (Z)
From Table 2 we can see that: 1) procurement indicators (Z1.1 and Z1.2) produced an average score of 609.1 in the very good category; 2) development indicators (Z1.3 and Z1.4) gave an average score of 605.1 in the very good category; 3) compensation indicators (Z1.5 and Z1.6) have an average score of 674.3 in the very good category; 4) maintenance indicators (Z1.7 and Z1.8) have an average score of 576.4 in the very good category; 5) discipline indicators (Z1.9 and Z1.10) have an average score of 624.1 in the very good category, and 6) termination indicators (Z1.11 and Z1.12) have an average score of 634.5 in the very good category.
3.3. Employee Performance Variable (Y)
Table 3 shows the respondents’ descriptive frequency of the employee performance variable (Y). We can see that: 1) quality indicators (Y1.1 and Y1.2) given by the respondents have an average score of 646.8 in the very good category; 2) quantity indicators (Y1.3 and Y1.4) have an average score of 557.8 in the very good category; 3) punctuality indicators (Y1.5 and Y1.6) have an average score of 642.7 in the very good category; 4) effectiveness ndicators (Y1.7 and Y1.8) have an average score of 683.4 in the very good category, and 5) independence indicators (Y1.9 and Y1. 10) have an average score of 660.6 in the very good category.
Table-1. Respondents’ Summary of the Human Resource Development Variable (X).
| Work Performance | ||||||||||||
| Indicator | Strongly Disagree |
Disagree |
Enough |
Agree |
Strongly Agree |
Sum item |
Average |
|||||
| Score | 1 |
F |
2 |
F |
3 |
F |
4 |
F |
5 |
F |
||
| Item 1 | 1 |
1.0 |
0 |
0.0 |
1 |
1.0 |
56 |
56.0 |
42 |
42.0 |
100 |
230.1 |
| Indicator Average | 230.1 |
|||||||||||
| Discipline | ||||||||||||
| Indicator | Strongly Disagree |
Disagree |
Enough |
Agree |
Strongly Agree |
Sum item |
Average |
|||||
| Score | 1 |
F |
2 |
F |
3 |
F |
4 |
F |
5 |
F |
||
| Item 2 | 0 |
0.0 |
4 |
2.3 |
1 |
0.6 |
66 |
38.6 |
29 |
58.5 |
100 |
280.0 |
| Indicator Average | 280.0 |
|||||||||||
| Attendance | ||||||||||||
| Indicator | Strongly Disagree |
Disagree |
Enough |
Agree |
Strongly Agree |
Sum item |
Average |
|||||
| Score | 1 |
F |
2 |
F |
3 |
F |
4 |
F |
5 |
F |
||
| Item 3 | 0 |
0.0 |
2 |
2.0 |
6 |
6.0 |
58 |
58.0 |
34 |
34.0 |
100 |
255.7 |
| Indicator Average | 255.7 |
|||||||||||
| Accident Rate | ||||||||||||
| Indicator | Strongly Disagree |
Disagree |
Enough |
Agree |
Strongly Agree |
Sum item |
Average |
|||||
| Score | 1 |
F |
2 |
F |
3 |
F |
4 |
F |
5 |
F |
||
| Item 4 | 0 |
0.0 |
4 |
4.0 |
10 |
10.0 |
61 |
61.0 |
25 |
25.0 |
100 |
283.3 |
| Indicator Average | 283.3 |
|||||||||||
| The Cooperation level | ||||||||||||
| Indicator | Strongly Disagree |
Disagree |
Enough |
Agree |
Strongly Agree |
Sum item |
Average |
|||||
| Score | 1 |
F |
2 |
F |
3 |
F |
4 |
F |
5 |
F |
||
| Item 5 | 0 |
0.0 |
2 |
2.0 |
2 |
2.0 |
51 |
51.0 |
45 |
45.0 |
100 |
216.3 |
| Indicator Average | 216.3 |
|||||||||||
| Praaksara | ||||||||||||
| Indicator | Strongly Disagree |
Disagree |
Enough |
Agree |
Strongly Agree |
Sum item |
Average |
|||||
| Score | 1 |
F |
2 |
F |
3 |
F |
4 |
F |
5 |
F |
||
| Item 6 | 0 |
0.0 |
2 |
2.0 |
2 |
2.0 |
76 |
76.0 |
20 |
20.0 |
100 |
315.0 |
| Indicator Average | 315.0 |
|||||||||||
| Wage Rate | ||||||||||||
| Indicator | Strongly Disagree |
Disagree |
Enough |
Agree |
Strongly Agree |
Sum item |
Average |
|||||
| Score | 1 |
F |
2 |
F |
3 |
F |
4 |
F |
5 |
F |
||
| Item 7 | 0 |
0.0 |
2 |
2.0 |
8 |
8.0 |
77 |
77.0 |
13 |
13.0 |
100 |
336.7 |
| Indicator Average | 336.7 |
|||||||||||
| Leadership and Managerial Decisions | ||||||||||||
| Indicator | Strongly Disagree |
Disagree |
Enough |
Agree |
Strongly Agree |
Sum item |
Average |
|||||
| Score | 1 |
F |
2 |
F |
3 |
F |
4 |
F |
5 |
F |
||
| Item8 | 0 |
0.0 |
2 |
2.0 |
12 |
12.0 |
70 |
70.0 |
16 |
16.0 |
100 |
320.8 |
| Indicator Average | 320.8 |
|||||||||||
Table-2. Summary of Responses on Human Resource Planning Variable (Z).
| Human Resource Planning (Z) | ||||||||||||
| Procurement | ||||||||||||
| Indicator | Strongly is agree |
Disagree |
Enough |
Agree |
Strongly Agree |
Sum item |
Average |
|||||
| Score | 1 |
F |
2 |
F |
3 |
F |
4 |
F |
5 |
F |
||
| Item 1 | 0 |
0.0 |
3 |
3.0 |
5 |
5.0 |
71 |
71.0 |
21 |
21.0 |
100 |
306.1 |
| Item 2 | 0 |
0.0 |
4 |
4.0 |
10 |
10.0 |
66 |
66.0 |
20 |
20.0 |
100 |
303.0 |
| Indicator Average | 609.1 |
|||||||||||
| Development | ||||||||||||
| Indicator | Strongly Disagree |
Disagree |
Enough |
Agree |
Strongly Agree |
Sum item |
Average |
|||||
| Score | 1 |
F |
2 |
F |
3 |
F |
4 |
F |
5 |
F |
||
| Item 3 | 0 |
0.0 |
1 |
1.0 |
3 |
3.1 |
73 |
74.5 |
21 |
21.4 |
98 |
304.1 |
| Item 4 | 0 |
0.0 |
4 |
4.0 |
8 |
8.0 |
67 |
67.0 |
21 |
21.0 |
100 |
301.1 |
| Indicator Average | 605.1 |
|||||||||||
| Compensation | ||||||||||||
| Indicator | Strongly Disagree |
Disagree |
Enough |
Agree |
Strongly Agree |
Sum item |
Average |
|||||
| Score | 1 |
F |
2 |
F |
3 |
F |
4 |
F |
5 |
F |
||
| Item 5 | 0 |
0.0 |
3 |
3.0 |
11 |
11.0 |
73 |
73.0 |
13 |
13.0 |
100 |
331.7 |
| Item 6 | 0 |
0.0 |
1 |
1.0 |
4 |
4.0 |
82 |
82.0 |
13 |
13.0 |
100 |
342.7 |
| Indicator Average | 674.3 |
|||||||||||
| Maintenance | ||||||||||||
| Indicator | Strongly Disagree |
Disagree |
Enough |
Agree |
Strongly Agree |
Sum item |
Average |
|||||
| Score | 1 |
F |
2 |
F |
3 |
F |
4 |
F |
5 |
F |
||
| Item 7 | 0 |
0.0 |
3 |
3.0 |
6 |
6.0 |
70 |
70.0 |
21 |
21.0 |
100 |
305.1 |
| Item 8 | 1 |
1.0 |
6 |
6.0 |
7 |
7.0 |
59 |
59.0 |
27 |
27.0 |
100 |
271.4 |
| Indicator Average | 576.4 |
|||||||||||
| Discipline | ||||||||||||
| Indicator | Strongly Disagree |
Disagree |
Enough |
Agree |
Strongly Agree |
Sum item |
Average |
|||||
| Score | 1 |
F |
2 |
F |
3 |
F |
4 |
F |
5 |
F |
||
| Item 9 | 0 |
0.0 |
0 |
0.0 |
4 |
4.0 |
74 |
74.0 |
22 |
22.0 |
100 |
309.1 |
| Item 10 | 0 |
0.0 |
1 |
1.0 |
4 |
4.0 |
75 |
75.0 |
20 |
20.0 |
100 |
315.0 |
| Indicator Average | 624.1 |
|||||||||||
| Termination | ||||||||||||
| Indicator | Strongly Disagree |
Disagree |
Enough |
Agree |
Strongly Agree |
Sum item |
Average |
|||||
| Score | 1 |
F |
2 |
F |
3 |
F |
4 |
F |
5 |
F |
||
| Item 11 | 1 |
1.0 |
4 |
4.0 |
11 |
11.0 |
70 |
70.0 |
14.0 |
14.0 |
100 |
322.7 |
| Item 12 | 0 |
0.0 |
7 |
7.0 |
11 |
11.0 |
66 |
66.0 |
16 |
16.0 |
100 |
311.8 |
| Indicator Average | 634.5 |
|||||||||||
3.4. Evaluation of the Measurement Model
- Results of Convergent Validity Analysis
Based on the results of convergent validity (see T able 4), the values of X.1 to X.8 indicate that the loading factor value is > 0.50, so the results from the SmartPLS program show that the results of the validity test on the eight items for the human resource development variable (X) have a convergent validity level greater than 0.5; these results indicate that the statements used in the questionnaire are invalid, namely X1.1, X1.2, and X1.3. These statements are omitted from the human resource development variable (X) and cannot be included in the next analysis. Furthermore, the values of questions Y1.1 to Y1.10 indicate that the loading factor value is > 0.50, so the results from the SmartPLS program show that the results of the validity test on the ten items for the employee performance variable (Y) are all valid. With a greater level of convergent validity (> 0.5), this indicates that the statement used in the questionnaire is invalid, namely Y1.6, so the statement cannot be used as an indicator of the employee performance variable (Y) and cannot be included in the next analysis.
The values of questions Z1.1 to Z1.12 indicate that the loading factor value is > 0.50. The results of data processing using the SmartPLS program show that the results of the validity test on the 12 questions on the human resource planning variable (Z) show a convergent validity greater than 0.5, which indicates that the statements used in the questionnaire are invalid, namely Z1.11 and Z1.12, and cannot be used as indicators of the human resource planning variable (Z) and cannot be included in the next analysis.
Table-3. Summary of respondents’ employee performance variable (Y).
| Employee Performance (Y) | |||||||||||||
| Quality | |||||||||||||
| Indicator | Strongly Disagree |
Disagree |
Enough |
Agree |
Strongly Agree |
Sum item |
Average |
||||||
| Score | 1 |
F |
2 |
F |
3 |
F |
4 |
F |
5 |
F |
|||
| Item 1 | 0 |
0.0 |
1 |
1.0 |
4 |
4.0 |
78 |
78.0 |
17 |
17.0 |
100 |
326.9 |
|
| Item 2 | 0 |
0.0 |
1 |
1.0 |
3 |
3.0 |
77 |
77.0 |
19 |
19.0 |
100 |
320.0 |
|
| Indicator Average | 646.8 |
||||||||||||
| Quantity | |||||||||||||
| Indicator | Strongly Disagree |
Disagree |
Enough |
Agree |
Strongly Agree |
Sum item |
Average |
||||||
| Score | 1 |
F |
2 |
F |
3 |
F |
4 |
F |
5 |
F |
|||
| Item 3 | 0 |
0.0 |
0 |
0.0 |
4 |
4.1 |
65 |
67.0 |
28 |
28.9 |
97 |
273.4 |
|
| Item 4 | 0 |
0.0 |
1 |
1.0 |
3 |
3.0 |
68 |
68.0 |
28 |
28.0 |
100 |
284.4 |
|
| Indicator Average | 557.8 |
||||||||||||
| Punctuality | |||||||||||||
| Indicator | Strongly Disagree |
Disagree |
Enough |
Agree |
Strongly Agree |
Sum item |
Average |
||||||
| Score | 1 |
F |
2 |
F |
3 |
F |
4 |
F |
5 |
F |
|||
| Item 5 | 0 |
0.0 |
1 |
1.0 |
3 |
3.0 |
76 |
76.0 |
20 |
20.0 |
100 |
316.0 |
|
| Item 6 | 0 |
0.0 |
3 |
3.0 |
16 |
16.0 |
68 |
68.0 |
13 |
13.0 |
100 |
326.7 |
|
| Indicator Average | 642.7 |
||||||||||||
| Effectiveness | |||||||||||||
| Indicator | Strongly Disagree |
Disagree |
Enough |
Agree |
Strongly Agree |
Sum item |
Average |
||||||
| Score | 1 |
F |
2 |
F |
3 |
F |
4 |
F |
5 |
F |
|||
| Item 7 | 0 |
0.0 |
0 |
0.0 |
5 |
5.0 |
85 |
85.0 |
10 |
10.0 |
100 |
355.5 |
|
| Item 8 | 0 |
0.0 |
1 |
1.0 |
3 |
3.0 |
79 |
79.0 |
17 |
17.0 |
100 |
327.9 |
|
| Indicator Average | 683.4 |
||||||||||||
| Independence | |||||||||||||
| Indicator | Strongly Disagree |
Disagree |
Enough |
Agree |
Strongly Agree |
Sum item |
Average |
||||||
| Score | 1 |
F |
2 |
F |
3 |
F |
4 |
F |
5 |
F |
|||
| Item 9 | 0 |
0.0 |
1 |
1.0 |
3 |
3.0 |
77 |
77.0 |
19 |
19.0 |
100 |
320.0 |
|
| Item 10 | 0 |
0.0 |
2 |
2.0 |
4 |
4.0 |
81 |
81.0 |
13 |
13.0 |
100 |
340.7 |
|
| Indicator Average | 660.6 |
||||||||||||
- Results of Discriminant Validity Analysis
Table 5 above shows the correlation values as follows: a) Human Resource Development (X) has a relationship of 0.591 with Employee Performance (Y); b) Human Resource Development (X) has a relationship of 0.677 with Human Resource Planning (Z); c) Human Resource Planning (Z) has a relationship of 0.648 with Employee Performance (Y); and d) the moderating effect of Human Resource Planning (Z) on Human Resource Development (X) is 0.309 with Employee Performance (Y). Based on this interpretation, it can be assumed that all latent variables in this study have a relationship of between 90% and 100%.
- Result of Construct Reliability Analysis
The construct reliability (see Table 6) shows the value of Cronbach's alpha, rho_A and the composite reliability of all research variables is more than > 0.7. These results indicate that rho_A and the composite reliability of all human resource development (X), human resource planning (Z) and employee performance (Y) variables used in this study are declared reliable.
Table-4. Measurement criteria for convergent validity.
| No | Item | Loading Factor Value | Convergent Validity Criteria | Information |
1 |
X1.1 |
0.285 |
> 0.5
|
Invalid |
2 |
X1.2 |
0.478 |
Invalid |
|
3 |
X1.3 |
0.415 |
Invalid |
|
4 |
X1.4 |
0.698 |
Valid |
|
5 |
X1.5 |
0.667 |
Valid |
|
6 |
X1.6 |
0.557 |
Valid |
|
7 |
X1.7 |
0.542 |
Valid |
|
8 |
X1.8 |
0.732 |
Valid |
|
9 |
Y1.1 |
0.784 |
Valid |
|
10 |
Y1.2 |
0.704 |
Valid |
|
11 |
Y1.3 |
0.705 |
Valid |
|
12 |
Y1.4 |
0.852 |
Valid |
|
13 |
Y1.5 |
0.759 |
Valid |
|
14 |
Y1.6 |
0.493 |
Invalid |
|
15 |
Y1.7 |
0.591 |
Valid |
|
16 |
Y1.8 |
0.804 |
Valid |
|
17 |
Y1.9 |
0.735 |
Valid |
|
18 |
Y1.10 |
0.735 |
Valid |
|
19 |
Z1.1 |
0.675 |
Valid |
|
20 |
Z1.2 |
0.723 |
Valid |
|
21 |
Z1.3 |
0.571 |
Valid |
|
22 |
Z1.4 |
0.569 |
Valid |
|
23 |
Z1.5 |
0.662 |
Valid |
|
24 |
Z1.6 |
0.554 |
Valid |
|
25 |
Z1.7 |
0.713 |
Valid |
|
26 |
Z1.8 |
0.609 |
Valid |
|
27 |
Z1.9 |
0.752 |
Valid |
|
28 |
Z1.10 |
0.634 |
Valid |
|
29 |
Z1.11 |
0.267 |
Invalid |
|
30 |
Z1.12 |
0.394 |
Invalid |
Table-5. Latent correlation variables.
| Moderating Variable | Y |
Moderation Z over X |
X |
Z |
|||
| Employee Performance (Y) | 1.000 |
0.309 |
0.591 |
0.648 |
|||
| Moderating Effect Z over X | 0.309 |
1.000 |
0.227 |
0.305 |
|||
| Human Resource Development (X) | 0.591 |
0.227 |
1.000 |
0.677 |
|||
| Human Resource Planning (Z) | 0.648 |
0.305 |
0.677 |
1.000 |
|||
| 31 | Z1.12 | 0.394 | Invalid |
||||
Table-6. Research variable construct reliability.
| Variable | Cronbach's Alpha |
Rho_A |
Composite Reliability |
Loading Factor > 0.70 |
Result |
| Employee Performance _ (Y) | 0.898 |
0.904 |
0.917 |
> 0.70 |
Reliability |
| Human Resource Planning _(Z) | 0.818 |
0.843 |
0.866 |
> 0.70 |
Reliability |
| Human Resources Development _ (X) | 0.754 |
0.764 |
0.834 |
> 0.70 |
Reliability |
3.5. Results of the Inner Model Residual Descriptive Test Analysis
In this study, the data normality was tested using the skewness and kurtosis tests. The decisions taken in the skewness and kurtosis tests is that if the ratio value of skewness and kurtosis is between -2 and +2, the data distribution is normal. Based on Table 7 above, the results of the skewness and kurtosis tests are between -2 and +2, showing that; a) employee performance (Y) has a standard deviation of 1.000, the skewness ratio is 0.015 and the kurtosis ratio is 2.100, so it can be concluded that the data distribution is normal; b) human resource development (X) has a standard deviation of 1.000, the value of the skewness ratio is -0.199 and the kurtosis ratio is 1.386, so it can be concluded that the data distribution is normal; c) human resources planning has a standard deviation of 1.000, the skewness ratio value is -0.009 and the kurtosis ratio is 1.499, so it can be concluded that the data distribution is normal; and d) the average standard deviation is 0.714, the skewness ratio value is -0.782 and the kurtosis ratio is 3.366, so it can be concluded that variables X, Z and Y, with a skewness of -2 and a kurtosis value of +2, show that the data distribution is normal.
Table-7. Results of the descriptive inner model residual.
| Variable | Standard Deviation |
Kurtosis |
Skewness |
Result |
| Employee Performance _ (Y) | 1.000 |
2.100 |
0.015 |
Normal |
| Human Resource Planning _(Z) | 1.000 |
1.386 |
-0.199 |
Normal |
| Human Resources Development _ (X) | 1.000 |
1.499 |
-0.009 |
Normal |
| Average | 0.714 |
3.366 |
-0.782 |
Normal |
3.6. Results of Structural Model Evaluation
This research will explains the results of the path coefficient test, goodness of fit test and hypothesis testing. The structural model, or inner model, is evaluated by looking at the percentage of variants described, namely by looking at R-squared for the dependent latent construct using the Q-squared test measurements of the SmartPLS output using the calculated PLS algorithm.
Figure-1. Structural model of calculation algorithm and path coefficients.
3.7. Designing the Inner Model
The goodness of fit model is measured using the R-squared of the dependent variable with the same interpretation as the regression. Q-squared predictive relevance for structural models, measures how well the observed value is generated by the model and also its parameter estimates. R-squared value > 0 indicates the model has the opposite predictive relevance if the R-squared value ≤ 0 indicates that the model lacks predictive relevance. The results of measuring the inner model with PLS are as follows:
Tabel-8. Results from calculation algorithm with SmartPLS R-Squared.
No. |
Variable |
R-squared |
Adjusted R-squared |
1. |
Employee Performance (Y) |
0.512 |
0.497 |
The results of the structural model testing (inner model) with the calculation algorithm show that the dependent variable can be explained by the independent variable on a moderate scale. Table 8 shows that the value of R-squared of the endogenous variable, namely employee performance (Y) of 0.512, the model is weak and the variable is able to explain employee performance by 51.2%. From this figure the dependent variable can be explained by the independent variable with a moderation scale.
Meanwhile, the goodness of fit is known from the Q-squared value. The results of the measurement of the inner model with PLS or adjusted R-squared value have a value between 0 and 1. If the adjusted R-squared value is closer to 1, it indicates that the human resource development variable (X) and human resource planning (Z) variations of the employee performance variable (Y) have an adjusted R-squared value of 0.497, which is more favorable.
3.8. Bootstrapping Test Results
Based on Table 9, the result shows that the value of first hypothesis, which examines the effect of human resource development on employee performance, shows that the original sample value is 0.425 and the t-statistic is 0.022. The measurement results show that 2,300> 1.96 (t-table significance level of 5% = 1.96) or p-values 0.022 < 0.05, the first hypothesis in this study is accepted. The results of these data, it can be interpreted that the human resource development variable has proved a significant influence on the employee performance variable with a positive relationship direction.
The second hypothesis, which examines the effect of human resource planning on employee performance, shows that the original sample value is 0.315 and the t-statistic is 3.922. The measurement results show that 3,922> 1.96 (t-table significance level of 5% = 1.96) or p-values 0.000 <0.05, the second hypothesis in this study is accepted. The results of these data, it can be interpreted that the human resource development variable has proved a significant influence on the employee performance variable with a positive relationship direction.
The third hypothesis that tests human resource planning moderate the effect of human resource development on employee performance, shows the original sample value (original sample) of 0.099 and t-statistic 1.430, The measurement results show that 1.430 <1.96 (t-table with a significance level of 5 % = 1.96), or p-values 0.153> 0.05, the third hypothesis in this study is rejected, or in other words, the value of 0.425 x 0,315 = 1,339 < 1.430. The results of the data processing above, it can be interpreted that the human resource planning variable is unable to moderate human resource development on employee performance with a positive relationship direction.
The results of the hypothesis bootstrapping test analysis show that human resource development has an effect on the performance of employees in the South Sumatra regional division, which means that it is in accordance with the first hypothesis that the development of resources on the performance employees; second, it can be interpreted that human resource planning (Z) affects the performance of the employees, which means that it is in accordance with the hypothesis that human resource planning affects the performance of the Perum Bulog employees; and third, it can be interpreted that that human resource planning is not a moderating variable for the development of human resources on the performance of employees at Perum Bulog. It is expected that Ho is rejected and Ha is accepted, or SmartPLS has a sig. value of < 0.05.
Table-9 . Test Results With Bootstrapping.
Result for Inner Weights Mean, STDEV, T-Values, P-Values.
| Variable | Original Sample (O) |
T Statistic (O/STDEV) |
P-Values |
| Human Resource Development (X) --> Employee Performance (Y) | 0.425 |
2,300 |
0,022 |
| Human Resource Planning (Z) --> Employee Performance (Y) | 0.315 |
3,922 |
0,000 |
| Moderating Effect Z over X --> Employee Performanced (Y) | 0,099 |
1,430 |
0,153 |
4. CONCLUSION
From the results of this study it can be concluded that the human resource development variable has an effect on the performance of the South Sumatra regional division employees; the test results show that human resource planning (Z) has an effect on the performance of regional officers of regional division in South Sumatra; and based on the results of the test, the human resource planning variable is not a moderating variable for the development of human resources on the performance of employees in the regional division of Perum Bulog, Indonesia.
Funding: This study received no specific financial support. |
Competing Interests: The authors declare that they have no competing interests. |
Acknowledgement: Authors would like to express their special thanks and gratitude to the Perum Bulog Regional Division of South Sumatra, Rector Universitas PGRI Palembang, the Director of the Graduate Program Universitas PGRI Palembang and the Study Program of Educational Management Universitas PGRI Palembang who supported this project, which was funded independently. |
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