Performance Evaluation of Fishery Enterprises Using Data Envelopment Analysis—A Malmquist Model
Abstract
:1. Introduction
2. Literature Review
3. Materials and Methods
3.1. Research Flow
3.2. Pearson’s Correlation Coefficients
3.3. DEA Malmquist Model
4. A Case Study
4.1. Decision-Making Units (DMUs) Selection
4.2. Inputs and Outputs Selection
- Total assets (TOAs): the total number of assets owned by a fishery company.
- Equity (EQU): the remaining amount of assets available to shareholders after all liabilities have been paid, equal assets subtract liabilities.
- Total liabilities (TOLs): the aggregate debt and financial obligations owned by fishery enterprises at any specific period.
- Cost of sales (COS): the accumulated total of all the costs used to create a product or service, which has been sold.
- Output variables:
- Revenue (REV): the total amount of money that will be earned by consuming products, providing services, financial activities, and other activities of the enterprises.
- Profit (PRO): profits earned by the company after deducting costs related to fishing and selling fish.
4.3. Data Collection
5. Results Analysis
5.1. Correlation Coefficients
5.2. DEA Malmquist Results
5.2.1. Catch-Up Index (CA)
5.2.2. Frontier-Shift Index (FR)
5.2.3. Malmquist Index (MI)
6. Discussions
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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No. | Authors | DEA CCR | DEA BCC | DEA SBM | DEA Window | DEA Malmquist | SPF | Tobit Regression | (Fuzzy) AHP | (Fuzzy) TOPSIS | (Fuzzy) COPRAS | (Fuzzy) CODAS |
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Vestergaard et al., 2003 [40] | x | ||||||||||
2 | Tingley et al., 2005 [27] | x | x | x | ||||||||
3 | Lindebo, 2005 [41] | x | ||||||||||
4 | Van Hoof and DeWilde, 2005 [42] | x | ||||||||||
5 | Walden, 2006 [43] | x | x | |||||||||
6 | Tsitsika et al., 2008 [44] | x | ||||||||||
7 | Wanke et al., 2011 [35] | x | x | |||||||||
8 | Alam, 2011 [36] | x | x | |||||||||
9 | Vassdal and Holst, 2011 [45] | x | ||||||||||
10 | Lim et al., 2012 [46] | x | x | |||||||||
11 | Vázquez-Rowe and Tyedmers, 2013 [28] | x | x | |||||||||
12 | Asche et al., 2013 [47] | x | ||||||||||
13 | Mustapha et al., 2013 [48] | x | ||||||||||
14 | Arita and Leung, 2014 [29] | x | x | |||||||||
15 | Ceyhan and Gene, 2014 [49] | x | ||||||||||
16 | Madau et al., 2018 [37] | x | ||||||||||
17 | Park et al., 2018 [38] | x | x | |||||||||
18 | Hassanpour and Pamucar, 2019 [30] | x | x | |||||||||
19 | Anthony et al., 2019 [31] | x | x | x | ||||||||
20 | Bayazid et al., 2019 [32] | x | x | x | ||||||||
21 | Li et al., 2020 [33] | x | x | |||||||||
22 | Gutiérrez et al., 2020 [39] | x | x | |||||||||
23 | Blagojević et al., 2020 [34] | x | x | x | ||||||||
24 | Deveci et al., 2020 [53] | x | ||||||||||
25 | Elkadeem et al., 2020 [52] | x | x | x | x | |||||||
26 | Zhang et al., 2020 [50] | x | ||||||||||
27 | Pan et al., 2020 [51] | x |
Order | DMU | Companies Name | Stock Code |
---|---|---|---|
1 | DMU1 | Mekong Fisheries Joint Stock Company | AAM |
2 | DMU2 | Ben Tre Aquaproduct Import and Export JSC | ABT |
3 | DMU3 | Cuu Long Fish Joint Stock Company | ACL |
4 | DMU4 | An Giang Fisheries Import Export JSC | AGM |
5 | DMU5 | Nam Viet Corporation | ANV |
6 | DMU6 | Camimex Group JSC | CMX |
7 | DMU7 | Sao Ta Foods Joint Stock Company | FMC |
8 | DMU8 | Hoang Long Group | HLG |
9 | DMU9 | Hung Vuong Joint Stock Corporation | HVG |
10 | DMU10 | Investment Commerce Fisheries Corporation | ICF |
11 | DMU11 | International Development & Investment Corporation | IDI |
12 | DMU12 | Seafood Joint Stock Company No4 | TS4 |
13 | DMU13 | Vinh Hoan Corporation | VHC |
14 | DMU14 | Bac Lieu Fisheries Joint Stock Company | BLF |
15 | DMU15 | Kien Hung JSC | KHS |
16 | DMU16 | Ngo Quyen Export Seafood Processing JSC | NGC |
17 | DMU17 | Hung Hau Agricultural Corporation | SJ1 |
Papers | Input Variables | Output Variables | Research Scope |
---|---|---|---|
Tingley et al., 2005 [27] | Annual day fished Engine power Overall length | Annual revenue | 3 DMUs United Kingdom |
Arita and Leung, 2014 [29] | Labor expense Number of employees Size of land | Total sales | 82 DMUs Hawaii |
Theodoridis et al., 2017 [59] | Farm size Labor Capital cost | Gross revenue | 66 DMUs Greece |
Madau et al., 2018 [37] | Materials cost Labor cost Production cost Capital endowment | Production value Net income | 104 DUMs Sea of Sardinia |
Li et al., 2020 [33] | Fish farms Ships Staff numbers | Fish catch Net income | 11 DMUs China |
Ding et al., 2020 [50] | Labor cost Capital investment | Gross ocean product | 11 DMUs China |
Gutiérrez et al., 2020 [39] | Number of employees Assets Livestock cost Operation cost | Production value | 18 DMUs Europe |
This paper | Total assets Equity Total liabilities Cost of sales | Revenue Profit | 17 DMUs Vietnam |
Period | Statistics | TOA | EQU | TOL | COS | REV | PRO |
---|---|---|---|---|---|---|---|
2015 | Max | 622,677 | 142,586 | 480,091 | 16,856 | 531,768 | 38,385 |
Min | 4703 | 959 | 3116 | 268 | 5323 | 939 | |
Avg. | 94,351 | 28,667 | 65,684 | 3755 | 91,917 | 9774 | |
SD | 146,824.0 | 37,968.2 | 111,760.5 | 4403.8 | 131,500.4 | 11,086.7 | |
2016 | Max | 715,647 | 140,808 | 574,840 | 22,142 | 770,876 | 58,170 |
Min | 4491 | 976 | 557 | 320 | 4807 | 754 | |
Avg. | 104,494 | 32,524 | 71,970 | 3969 | 118,384 | 11,449 | |
SD | 169,397.3 | 41,318.9 | 133,935.8 | 5544.1 | 186,427.1 | 16,080.0 | |
2017 | Max | 598,139 | 126,837 | 490,435 | 20,634 | 668,740 | 50,512 |
Min | 4597 | 1002 | 457 | 213 | 5493 | 366 | |
Avg. | 99,629 | 32,808 | 66,820 | 3987 | 119,528 | 11,821 | |
SD | 145,820.4 | 39,712.7 | 115,396.9 | 5282.4 | 169,095.2 | 15,510.0 | |
2018 | Max | 369,976 | 173,068 | 277,614 | 12,548 | 399,626 | 89,189 |
Min | 4626 | 1055 | 788 | 250 | 7725 | 1.000 | |
Avg. | 94,188 | 37,636 | 56,551 | 3344 | 113,826 | 16,485 | |
SD | 110,016.4 | 48,446.7 | 71,182.5 | 3625.0 | 123,704.5 | 22,622.2 |
TOA | EQU | TOL | COS | REV | PRO | ||
---|---|---|---|---|---|---|---|
Total assets (TOA) | Pearson correlation | 1 | 0.858 ** | 0.981 ** | 0.945 ** | 0.963 ** | 0.759 ** |
p-value | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
Sample size | 68 | 68 | 68 | 68 | 68 | 68 | |
Equity (EQU) | Pearson correlation | 0.858 ** | 1 | 0.740 ** | 0.868 ** | 0.883 ** | 0.932 ** |
p-value | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
Sample size | 68 | 68 | 68 | 68 | 68 | 68 | |
Total liabilities (TOL) | Pearson correlation | 0.981 ** | 0.740 ** | 1 | 0.904 ** | 0.923 ** | 0.637 ** |
p-value | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
Sample size | 68 | 68 | 68 | 68 | 68 | 68 | |
Cost of sales (COS) | Pearson correlation | 0.945 ** | 0.868 ** | 0.904 ** | 1 | 0.949 ** | 0.805 ** |
p-value | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
Sample size | 68 | 68 | 68 | 68 | 68 | 68 | |
Revenue (REV) | Pearson correlation | 0.963 ** | 0.883 ** | 0.923 ** | 0.949 ** | 1 | 0.844 ** |
p-value | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
Sample size | 68 | 68 | 68 | 68 | 68 | 68 | |
Profit (PRO) | Pearson correlation | 0.759 ** | 0.932 ** | 0.637 ** | 0.805 ** | 0.844 ** | 1 |
p-value | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
Sample size | 68 | 68 | 68 | 68 | 68 | 68 |
Catch-up | 2015–2016 | 2016–2017 | 2017–2018 | Average |
---|---|---|---|---|
DMU1 | 2.00923 | 0.93957 | 0.71915 | 1.22265 |
DMU2 | 0.46266 | 0.65018 | 1.89570 | 1.00285 |
DMU3 | 1.06983 | 0.97697 | 1.06537 | 1.03739 |
DMU4 | 1.63670 | 0.78556 | 0.90077 | 1.10768 |
DMU5 | 0.94129 | 1.21225 | 1.02382 | 1.05912 |
DMU6 | 1.40703 | 0.96565 | 0.61960 | 0.99743 |
DMU7 | 0.62399 | 1.47912 | 1.30108 | 1.13473 |
DMU8 | 1.91433 | 1.82901 | 0.73058 | 1.49131 |
DMU9 | 1.12230 | 1.13894 | 0.67123 | 0.97749 |
DMU10 | 0.58969 | 0.63994 | 2.64071 | 1.29011 |
DMU11 | 0.63122 | 1.72734 | 0.87598 | 1.07818 |
DMU12 | 0.95978 | 1.03593 | 0.99720 | 0.99764 |
DMU13 | 1.55202 | 1.01652 | 0.83853 | 1.13569 |
DMU14 | 0.78357 | 0.88487 | 0.66860 | 0.77901 |
DMU15 | 0.78096 | 1.25688 | 0.74675 | 0.92820 |
DMU16 | 1.17335 | 0.87917 | 1.30037 | 1.11763 |
DMU17 | 0.47763 | 1.21910 | 1.01167 | 0.90280 |
Average | 1.06680 | 1.09629 | 1.05924 | 1.07411 |
Max | 2.00923 | 1.82901 | 2.64071 | 1.49131 |
Min | 0.46266 | 0.63994 | 0.61960 | 0.77901 |
Frontier | 2015–2016 | 2016–2017 | 2017–2018 | Average |
---|---|---|---|---|
DMU1 | 1.10090 | 0.98679 | 1.56698 | 1.21822 |
DMU2 | 1.13555 | 1.02020 | 1.44704 | 1.20093 |
DMU3 | 0.99140 | 0.98482 | 1.70087 | 1.22570 |
DMU4 | 1.01858 | 1.01603 | 1.02068 | 1.01843 |
DMU5 | 1.11979 | 0.99227 | 1.49384 | 1.20196 |
DMU6 | 0.91846 | 1.01715 | 1.39101 | 1.10887 |
DMU7 | 1.05126 | 0.95858 | 1.09797 | 1.03594 |
DMU8 | 1.32646 | 0.75311 | 1.09002 | 1.05653 |
DMU9 | 1.14453 | 0.92009 | 0.97268 | 1.01243 |
DMU10 | 1.11779 | 0.98020 | 1.49067 | 1.19622 |
DMU11 | 1.13458 | 0.92352 | 1.53585 | 1.19798 |
DMU12 | 0.92113 | 1.08192 | 1.28773 | 1.09693 |
DMU13 | 0.94667 | 0.99792 | 1.59941 | 1.18133 |
DMU14 | 0.92834 | 1.01640 | 2.08939 | 1.34471 |
DMU15 | 1.04349 | 0.93943 | 1.28159 | 1.08817 |
DMU16 | 0.90836 | 1.00011 | 1.21763 | 1.04203 |
DMU17 | 1.78698 | 0.79241 | 1.39858 | 1.32599 |
Average | 1.09378 | 0.96359 | 1.39306 | 1.15014 |
Max | 1.78698 | 1.08192 | 2.08939 | 1.34471 |
Min | 0.90836 | 0.75311 | 0.97268 | 1.01243 |
Malmquist | 2015–2016 | 2016–2017 | 2017–2018 | Average |
---|---|---|---|---|
DMU1 | 2.21195 | 0.92716 | 1.12690 | 1.42200 |
DMU2 | 0.52538 | 0.66331 | 2.74316 | 1.31062 |
DMU3 | 1.06063 | 0.96214 | 1.81205 | 1.27828 |
DMU4 | 1.66710 | 0.79816 | 0.91940 | 1.12822 |
DMU5 | 1.05405 | 1.20287 | 1.52943 | 1.26211 |
DMU6 | 1.29230 | 0.98221 | 0.86187 | 1.04546 |
DMU7 | 0.65598 | 1.41786 | 1.42855 | 1.16746 |
DMU8 | 2.53928 | 1.37745 | 0.79634 | 1.57103 |
DMU9 | 1.28450 | 1.04793 | 0.65289 | 0.99511 |
DMU10 | 0.65915 | 0.62727 | 3.93644 | 1.74095 |
DMU11 | 0.71617 | 1.59522 | 1.34538 | 1.21892 |
DMU12 | 0.88409 | 1.12079 | 1.28413 | 1.09633 |
DMU13 | 1.46926 | 1.01440 | 1.34115 | 1.27494 |
DMU14 | 0.72742 | 0.89938 | 1.39696 | 1.00792 |
DMU15 | 0.81493 | 1.18075 | 0.95703 | 0.98423 |
DMU16 | 1.06582 | 0.87927 | 1.58337 | 1.17615 |
DMU17 | 0.85351 | 0.96602 | 1.41489 | 1.07814 |
Average | 1.14597 | 1.03895 | 1.47823 | 1.22105 |
Max | 2.53928 | 1.59522 | 3.93644 | 1.74095 |
Min | 0.52538 | 0.62727 | 0.65289 | 0.98423 |
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Wang, C.-N.; Nguyen, T.-L.; Dang, T.-T.; Bui, T.-H. Performance Evaluation of Fishery Enterprises Using Data Envelopment Analysis—A Malmquist Model. Mathematics 2021, 9, 469. https://doi.org/10.3390/math9050469
Wang C-N, Nguyen T-L, Dang T-T, Bui T-H. Performance Evaluation of Fishery Enterprises Using Data Envelopment Analysis—A Malmquist Model. Mathematics. 2021; 9(5):469. https://doi.org/10.3390/math9050469
Chicago/Turabian StyleWang, Chia-Nan, Thi-Ly Nguyen, Thanh-Tuan Dang, and Thi-Hong Bui. 2021. "Performance Evaluation of Fishery Enterprises Using Data Envelopment Analysis—A Malmquist Model" Mathematics 9, no. 5: 469. https://doi.org/10.3390/math9050469
APA StyleWang, C.-N., Nguyen, T.-L., Dang, T.-T., & Bui, T.-H. (2021). Performance Evaluation of Fishery Enterprises Using Data Envelopment Analysis—A Malmquist Model. Mathematics, 9(5), 469. https://doi.org/10.3390/math9050469