# A Performance Evaluation Study of Human Resources in Low-Carbon Logistics Enterprises

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## Abstract

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## 1. Introduction

## 2. Human Resource Features of Low-Carbon Logistics Companies and the Significance of Establishing a Performance Evaluation System

#### 2.1. Particular Human Resources in Carbon Logistics Companies

- (1)
- Low-carbon technologies. The logistics operation system includes transportation, warehousing, distribution, handling, and packaging [3,4]. Transportation uses networks to determine the best routes, warehousing uses research optimization theory to determine optimal inventory levels, and so forth. The various subsystems of logistics systems require low-carbon technologies for support; therefore, their human resources should have certain low-carbon technologies.
- (2)
- Low-carbon concept. Low-carbon logistics is a new trend in the development of the logistics industry. It requires integrating the low-carbon concept in the process of logistics system improvement, with consideration of environmental and energy issues, to help enterprises attain economic benefits while also protecting the environment [5]. Therefore, the concept of low carbon is necessary for the work-skills component of human resources in low-carbon logistics enterprises.
- (3)
- Strategic vision. The future development trend of low-carbon logistics will involve the whole supply chain, not just a single logistics enterprise. Supply chains themselves save costs. Low carbon is involved in the procurement of raw materials, product manufacturing, transport, and packaging. A series of links will be integrated into low carbon, and the whole supply chain will have a two-pronged effect. The sustainable development of supply chain trends and the human resource requirements of enterprises must have a strategic vision for global efforts. Such a vision will be put toward greater output while forming the competitive core of enterprises.
- (4)
- Innovation consciousness. Since the logistics industry is knowledge-and-talent intensive, competition between enterprises is growing. Knowledge renewal and technology innovation are necessary for achieving sustainable development. Thus, human resources in low-carbon logistics enterprises have a strong sense of innovation.

#### 2.2. The Significance of Human Resources Performance Evaluation in Low-Carbon Logistics Enterprises

- (1)
- For the general staff. Through performance evaluation, employees can see the results of their hard work and know their strengths and weaknesses, as well as their development potential, while better understanding the enterprise’s objectives.
- (2)
- For the supervisor. Based on the results of performance evaluations, managers can allocate and transfer human resources and determine remuneration. Employee evaluation is conducted to provide targeted training or promotion for outstanding officers.
- (3)
- For the organization. Performance evaluation is an important means for achieving an organization’s strategic objectives. It guides employee behavior and organizational goals. Performance evaluation is a central part of performance management. Evaluations not only show the results of the first phase of a performance plan but also provide a reference for improving the next program. By establishing a human resource performance appraisal system for low-carbon logistics enterprises, an enterprise’s previous work standard can be measured. This can help enterprises understand their development status and improve their plans for determining the best human resource management and development decisions. It plays a guiding role in the future development of an enterprise.

## 3. Building a Performance Evaluation System for Human Resources in Low-Carbon Logistics Enterprises

#### 3.1. Establishing an Evaluation Matrix Based on AHP

#### 3.1.1. Index Options and Index Hierarchical Model Construction

#### 3.1.2. Construction of the Judgment Matrix

_{ij}> 1, index i is more important for the target than index j; its numerical size represents an important extent.

#### 3.1.3. Single-Level Sorting and Determination of Index Weight

_{max}of judgment matrix A, and then use the formula $A\omega ={\eta}_{\mathrm{max}}\omega $ to obtain eigenvector ω corresponding to η

_{max}. After standardization, the sorted weight of the relative importance of certain factors is on the same level of the element corresponding to the previous level [7,8,9]. For the solution of the maximum eigenvalues and eigenvectors of the judgment matrix, the obtained eigenvector ω is the sorted weight of the relative importance of certain factors, which is the same level as the element that corresponds to the previous level. We can use geometric mean normalization to normalize ω: ${\omega}_{i}=\sqrt[n]{{\omega}_{i}{\omega}_{2}\cdots {\omega}_{n}}$ and can obtain the approximate eigenvector ${\omega}_{i}^{\prime}=\frac{{\omega}_{i}}{\sqrt[n]{{\omega}_{1}{\omega}_{2}\cdots {\omega}_{n}}}$; ${\omega}_{i}^{\prime}$ is the sorted weight of relative importance after normalization, and n represents the number of eigenvalues in the judgment matrix.

#### 3.1.4. Consistency Check

#### 3.1.5. Determine the Evaluation Grade

#### 3.1.6. Evaluation Matrix Established by Assessment Factors

_{ij}Then, we can construct the d

_{ij}evaluation matrix D

_{i}of the performance evaluation for the first-level evaluation index A

_{i}, such that ${D}_{i}=\left(\begin{array}{c}{A}_{i1}\\ {A}_{i2}\\ \vdots \\ {A}_{in}\end{array}\right)=\left(\begin{array}{cccc}{d}_{i11}& {d}_{i11}& \cdots & {d}_{i1p}\\ {d}_{i21}& {d}_{i21}& \cdots & {d}_{i2p}\\ \vdots & \vdots & \vdots & \vdots \\ {d}_{in1}& {d}_{in2}& \cdots & {d}_{inp}\end{array}\right)$, where n is the index number of second-level evaluation index A

_{ij}.

#### 3.1.7. Notes

- (1)
- simplify the problem to grasp the main factors, not leakage; and
- (2)
- pay attention to the strength of the relationship between elements; the difference between the elements cannot be too much at the same level.

#### 3.2. A Gray Comprehensive Evaluation Method Based on Central Point Triangle Whitening Weight Function

#### 3.2.1. Construction of the Triangle Whiten Function

_{1}, λ

_{2}, ⋯, λ

_{s}are chosen as belonging to the gray class 1, 2, …, point s (the center point means that the selection is based on the maximum likelihood of belonging to the gray class). The value range of each index is accordingly divided into s gray classes, such as dividing the value range of index A

_{ij}into s small sections $\left[{\lambda}_{1},{\lambda}_{2}\right],\cdots ,\left[{\lambda}_{k-1},{\lambda}_{k}\right],\cdots \left[{\lambda}_{s-1},{\lambda}_{s}\right],\left[{\lambda}_{s},{\lambda}_{s+1}\right]$; the value of ${\lambda}_{k}(k=1,2,\cdots s,s+1)$ is determined in accordance with the requirements of practical problems or qualitative research results [10]. At the same time, point (λ

_{k}, 1) is connected to the center point (λ

_{k−}

_{1}, 0) of the k − 1 section and (λ

_{k}, 1) is connected to the center point (λ

_{k}

_{+1}, 0) of the k + 1 section to obtain the index A

_{ij}, the triangle whiten function ${f}_{k}(\u2022),k=1,2,\cdots ,s$, with respect to gray cluster k . The extent of the A

_{ij}index number field to the left of λ

_{0}and the right of λ

_{s+1}, to obtain the triangle whiten function ${f}_{1}(\u2022)$ and ${f}_{s}(\u2022)$ of A

_{ij}related to gray cluster 1 and s, is shown in Figure 1 [25,26].

_{ij}, we can use the formula

#### 3.2.2. Calculating the Gray Factor Evaluation Vector and Evaluation Matrix

_{ij}are ${d}_{ij1},{d}_{ij2},\cdots {d}_{ijp}$. Therefore, the whitening weight of index A

_{ij}belonging to the number k evaluation of the gray cluster considered by evaluators is ${f}_{k}({d}_{ij1}),{f}_{k}({d}_{ij2}),\cdots ,{f}_{k}({d}_{ijp})$. The total whiten function of A

_{ij}belonging to the number k evaluation of the gray cluster considered by the total evaluators is ${y}_{ijk}={\displaystyle \sum _{l=1}^{p}{f}_{k}({d}_{ijl})}$, and the total whitening weight of A

_{ij}belonging to each evaluation of the gray cluster is ${y}_{ij}={\displaystyle \sum _{k=1}^{s}{\displaystyle \sum _{l=1}^{p}{f}_{k}({d}_{ijl})}}$. The ratio between the two is ${r}_{ijk}={\displaystyle \sum _{l=1}^{p}{f}_{k}({d}_{ijl})}/{\displaystyle \sum _{k=1}^{s}{\displaystyle \sum _{l=1}^{p}{f}_{k}({d}_{ijl})}}$. Its size reflects the strong degree to which all evaluators consider the index A

_{ij}belonging to the number k gray cluster. This value is the gray evaluation coefficient of index A

_{ij}belonging to the number k gray cluster marked as r

_{ijk}. Vector r

_{ij}contains the gray evaluation coefficient of each gray cluster where index A

_{ij}belongs to its gray evaluation vector ${r}_{ijk}=({r}_{ij1},{r}_{ij2},\cdots ,{r}_{ijs})$, i = 1, 2, …, m; j = 1, 2, …, n; s is divided by the number of the gray cluster. The gray evaluation weight vector of the gray evaluation cluster of A

_{i}belonging to index A

_{ij}is summed to obtain the gray evaluation matrix of index A

_{i}:

#### 3.2.3. Calculating the Comprehensive Evaluation Value and Sorting

_{i}as the result of the comprehensive evaluation of index A

_{i}, and ${C}_{i}={U}_{i}\u2022{R}_{i}=({c}_{i1},{c}_{i2},\cdots ,{c}_{is})$. From C

_{i}, we can obtain the gray evaluation weight R of each gray evaluation cluster related to the performance evaluation A that belongs to index A

_{i}[27,28]. Then, the comprehensive evaluation of the results can be obtained; C:

## 4. Examples of Application and Discussion

_{11}belonging to the first gray cluster are obtained: ${y}_{111}=f(3)+f(5)+f(3)+f(4)+f(3)=4.00$. The whitening weights of the second gray cluster are similarly obtained: ${y}_{112}=2.67$. The whitening weights of the third gray cluster are ${y}_{11}=4.00+2.67+0.33=7.00$. As the total whitening weights of A

_{11}are ${y}_{11}=4.00+2.67+0.33=7.00$, the evaluation coefficient of A

_{11}belonging to the first gray cluster is ${r}_{111}=\frac{{y}_{111}}{{y}_{11}}=\frac{4}{7}=0.57$. The same method is used to obtain ${r}_{112}=0.38$; ${r}_{113}=0.05$, and the gray evaluation vector obtained is ${r}_{11}=(0.57,0.38,0.05)$. The same approach is used to obtain ${r}_{12}=(0.58,0.37,0.05)$; ${r}_{13}=(0.57,0.38,0.05)$. Then, the gray evaluation matrix is obtained: ${R}_{1}=\left(\begin{array}{ccc}0.57& 0.38& 0.05\\ 0.58& 0.37& 0.05\\ 0.57& 0.38& 0.05\end{array}\right)$. The same approach is used to obtain

_{1}is C

_{1}, where

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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Working Ability A_{1} | Weighting factor U_{1} | Low-carbon professional knowledge A_{11} | Weighting factor U_{11} |

Low-carbon professional skill A_{12} | Weighting factor U_{12} | ||

Low-carbon innovation potential A_{13} | Weighting factor U_{13} | ||

Working Performance A_{2} | Weighting factor U_{2} | Quantity of task completion A_{21} | Weighting factor U_{21} |

Quality of task completion (whether the low-carbon index is achieved) A_{22} | Weighting factor U_{22} | ||

Efficiency of task completion (whether the use of low-carbon skills improved efficiency) A_{23} | Weighting factor U_{23} | ||

Working Attitude A_{3} | Weighting factor U_{3} | Discipline A_{31} | Weighting factor U_{31} |

Cooperation (whether seen as part of low-carbon thinking) A_{32} | Weighting factor U_{32} | ||

Enthusiasm A_{33} | Weighting factor U_{33} |

_{i}represents the first index, U

_{i}represents the weights of the corresponding first index, A

_{ij}represents the second index under the corresponding first index, and U

_{ij}represents the weights of the corresponding second index (i = 1, 2, 3; j = 1, 2, 3; $\sum _{i=1}^{3}{U}_{i}=1$, $\sum _{i=1}^{3}{\displaystyle \sum _{j=1}^{3}{U}_{ij}=1}$).

Factor A: Factor B Ratio | Compare Quantized Value |
---|---|

Factors A and B are equally important | 1 |

A slightly more important factor than B | 3 |

A more important factor than B | 5 |

A very important factor compared to B | 7 |

A factor is definitely more important than B | 9 |

AB adjacent judgment intermediate value | 2, 4, 6, 8 |

Backward count of the upper figure is the reciprocal comparison of the two factors |

n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
---|---|---|---|---|---|---|---|---|---|

R.I | 0 | 0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 |

Grade | Excellent | Good | Moderate | Poor | Very Poor |
---|---|---|---|---|---|

Points | 9 | 7 | 5 | 3 | 1 |

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## Share and Cite

**MDPI and ACS Style**

Qu, Q.; Wang, W.; Tang, M.; Lu, Y.; Tsai, S.-B.; Wang, J.; Li, G.; Yu, C.-L.
A Performance Evaluation Study of Human Resources in Low-Carbon Logistics Enterprises. *Sustainability* **2017**, *9*, 632.
https://doi.org/10.3390/su9040632

**AMA Style**

Qu Q, Wang W, Tang M, Lu Y, Tsai S-B, Wang J, Li G, Yu C-L.
A Performance Evaluation Study of Human Resources in Low-Carbon Logistics Enterprises. *Sustainability*. 2017; 9(4):632.
https://doi.org/10.3390/su9040632

**Chicago/Turabian Style**

Qu, Qunzhen, Wenjing Wang, Mengxue Tang, Youhu Lu, Sang-Bing Tsai, Jiangtao Wang, Guodong Li, and Chih-Lang Yu.
2017. "A Performance Evaluation Study of Human Resources in Low-Carbon Logistics Enterprises" *Sustainability* 9, no. 4: 632.
https://doi.org/10.3390/su9040632